{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5b036017",
   "metadata": {},
   "source": [
    "# 一元回归分析\n",
    "$$\n",
    "    \\hat y = \\beta_{0} + \\beta_{1}x + \\epsilon\n",
    "$$\n",
    "我们假设\n",
    "1. 所有自变量$x$的误差$\\epsilon$互相独立\n",
    "2. 所有自变量$x$的误差$\\epsilon$服从均值为0，方差为$\\sigma^2$的正态分布"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8cb56f4",
   "metadata": {},
   "source": [
    "## 统计量计算\n",
    "### $\\sigma^2$\n",
    "$\\sigma^2$的样本估计值为样本方差$s^2$\n",
    "1. $s^2$ 直观上应该由SSE构造\n",
    "$$\n",
    "    \\begin{array}{ll}\n",
    "    s^2 = \\frac{SSE}{(n-2)} \\\\\n",
    "    则我们可以构造出一个卡方分布 \\\\\n",
    "    \\chi^2 = \\frac{SSE}{\\sigma^2} = \\frac{(n-2)s^2}{\\sigma^2} \\\\\n",
    "    最后得出 \\\\\n",
    "    E(s^2) = \\sigma^2 \\\\\n",
    "    \\end{array}\n",
    "$$\n",
    "\\* 即$s^2$是$\\sigma^2$的无偏估计 <br>\n",
    "2. 使用$s$能够确定拟合数据分布在哪个区间段中\n",
    "\n",
    "### $\\hat \\beta$\n",
    "1. $\\hat \\beta$ 是 $\\beta$的无偏估计\n",
    "2. $\\hat \\beta$ 的标准误差是\n",
    "$$\n",
    "    \\sigma_{\\hat \\beta_{i}} = \\sigma \\cdot {{(X^TX)}^{-1}}_{ii}\n",
    "$$\n",
    "\\* 其中${(X^TX)}^{-1}$是协方差矩阵的相应对角线位置值，也就是第$i$个变量的方差值"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a513714",
   "metadata": {},
   "source": [
    "## 最小二乘法 LSM\n",
    "注意，总体 $y$ 的样本平均值为 $\\bar y$，预测值为 $\\hat y$<br>\n",
    "(Least Squared Method)\n",
    "$$\n",
    "    \\hat y = \\beta_{0} + \\beta_{1}x\n",
    "$$\n",
    "使用MSE(Mean Squared Error)进行误差分析<br>\n",
    "MSE的表现形式与SSE相同，即\n",
    "$$\n",
    "    SSE = \\sum^{N}_{i}{{(y_{i}-\\hat y_{i})}^2}\n",
    "$$\n",
    "这时，最小二乘估计公式为：\n",
    "$$\n",
    "\\left\\{\n",
    "\\begin{array}{ll}\n",
    "\\hat \\beta_{1} = \\frac{SS_{xy}}{SS_{xx}} \\\\\n",
    "\\hat \\beta_{0} = \\bar y - \\hat \\beta_{1} \\bar x \\\\\n",
    "SS_{xy} = \\sum_{i}{x_{i}y_{i}}-\\frac{(\\sum_{i}{x_{i}})(\\sum_{i}{y_{i}})}{n} \\\\\n",
    "SS_{xx} = \\sum_{i}{{x_{i}}^2} - \\frac{{(\\sum_{i}{x_{i}})}^2}{n}\\\\\n",
    "\\end{array}\n",
    "\\right .\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "297703e0",
   "metadata": {},
   "source": [
    "## 矩阵形式\n",
    "参数说明：\n",
    "$$\n",
    "\\begin{array}{ll}\n",
    "    X ~ 自变量矩阵 \\\\\n",
    "    Y ~ 因变量矩阵\\\\\n",
    "    \\hat \\beta ~ OLS参数矩阵\\\\\n",
    "\\end{array}\n",
    "$$\n",
    "公式：\n",
    "$$\n",
    "\\begin{array}{ll}\n",
    "    \\hat \\beta = {(X^TX)}^{-1}X^TY \\\\\n",
    "    SSE = Y^TY - {\\hat \\beta}^TX^TY \\\\\n",
    "    s^2 = MSE = \\frac{SSE}{n-(k+1)} \\\\\n",
    "    * k为自变量数量\n",
    "\\end{array}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f95d1746",
   "metadata": {},
   "source": [
    "## 最小二乘法参数分析\n",
    "1. $\\hat \\beta_{1}$是$\\beta_{1}$的无偏估计，即\n",
    "$$\n",
    "    E[\\hat \\beta_{1}] = \\beta_{1}\n",
    "$$\n",
    "2. $\\hat \\beta_{1}$ 的方差为\n",
    "$$\n",
    "    V[\\hat \\beta_{1}] = \\frac{\\sigma^2}{SS_{xx}}\n",
    "$$\n",
    " \\* 其中，$\\sigma$是随机变量$y$的标准差，是一个总体值<br>\n",
    "3. 当总体方差$\\sigma^2$未知时，我们可以通过样本方差$s^2$来进行估计\n",
    "$$\n",
    "\\left \\{\n",
    "\\begin{array}{ll}\n",
    "s^2 = \\frac{SSE}{n-2}\\\\\n",
    "SSE = SS_{yy} - \\hat \\beta_{1} SS_{xy} \\\\\n",
    "SS_{yy} = \\sum_{i}{{y_{i}}^2} - \\frac{{(\\sum_{i}{y_{i}})}^2}{n}\\\\\n",
    "SS_{xy} = \\sum_{i}{x_{i}y_{i}}-\\frac{(\\sum_{i}{x_{i}})(\\sum_{i}{y_{i}})}{n} \\\\\n",
    "\\end{array}\n",
    "\\right .\n",
    "$$\n",
    "4. 此时，$\\hat \\beta_{1}$方差的无偏估计为\n",
    "$$\n",
    " V[\\hat \\beta_{1}] = \\frac{s^2}{SS_{xx}}\n",
    "$$\n",
    "5. 构造的t-student检验统计量为\n",
    "\n",
    "$$\n",
    "\\begin{array}{ll}\n",
    "    H_{0} : \\beta_{1}=0 \\\\\n",
    "    - 该值说明我们使用\\hat y 进行线性估计时\\beta_{1}的大小 \\\\\n",
    "    - 一般使用\\beta_{1}=0，即直接使用平均值\\bar y进行拟合分析 \\\\\n",
    "    H_{1} : \\beta_{1}\\ne 0 \\\\\n",
    "    T_{c} = \\frac{\\hat \\beta_{1} - 0}{s /\\sqrt{SS_{xx}}}\n",
    "\\end{array}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c5b7ee9",
   "metadata": {},
   "source": [
    "## 相关系数\n",
    "皮尔逊相关系数计算公式<br>\n",
    "$r$的正负决定正相关与负相关；绝对值决定相关性的大小\n",
    "$$\n",
    "    r = \\frac{SS_{xy}}{\\sqrt{SS_{xx}SS_{yy}}}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77eef62f",
   "metadata": {},
   "source": [
    "## 决定系数\n",
    "如果使用平均值$\\bar y$进行平行于x轴的线性估计，我们的残差平方和为\n",
    "$$\n",
    "    SS_{yy} = \\sum_{i}{{(y_{i}-\\bar y)}^2}\n",
    "$$\n",
    "\n",
    "实际上 $SS_{yy}$ 即为 $SST$\n",
    "\n",
    "如果使用预测值$\\hat y$进行有斜率的线性估计，我们的离差平方和为\n",
    "$$\n",
    "    SSE = \\sum_{i}{{(y_{i}-\\hat y)}^2}\n",
    "$$\n",
    "决定系数$r^2$说明$y$关于预测值$\\hat y$能够归因于$x$与$y$线性关系的比例，这个值越大说明线性关系贡献的比率越大\n",
    "$$\n",
    "    r^2 = 1 - \\frac{SSE}{SS_{yy}}\n",
    "$$\n",
    "如果$r^2$较小，那么我们有理由认为$x$对于$y$的线性贡献值较小，此时应当考虑其他的自变量取值"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f80bc676",
   "metadata": {},
   "source": [
    "## 利用模型进行估计和预测\n",
    "一共分为两个方面：<br>首先是对于多次预测取平均值的预测即$E[y]$的预测；<br>其次是对于单个点$y$的预测<br>\n",
    "在预测过程中，仍旧通过构造学生式t分布的方法进行置信区间的计算<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5b5c214",
   "metadata": {},
   "source": [
    "## 残差分析\n",
    "残差分析即是对于$\\epsilon$的分析，为了确保对于$\\epsilon$的假设是正确的\n",
    "1. 检验期望：绘制残差图$(y - \\hat y) - x$\n",
    "2. 检验方差：绘制残差图$(y - \\hat y) - \\hat y$\n",
    "3. 检验正态分布： 绘制茎叶图或是直方图\n",
    "4. 检验相互独立： 绘制残差-时间序列对照图"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b3d47eb",
   "metadata": {},
   "source": [
    "## statsmodels实例\n",
    "1. 回归分析将会涉及到统计学的t检验和F检验，因此需要使用<font color=maroon><b>statsmodels</b></font>工具包\n",
    "2. t检验-如前所述，是对单独一个参数进行显著性检验；$H_0$=该参数为0，我们期待p值足够小以拒绝原假设而支持备择假设\n",
    "3. F检验-实际上是对所有参数进行显著性检验； $H_0$=所有参数均为0，我们期待p值足够小以拒绝原假设而支持备择假设\n",
    "4. <font color=maroon><b>statsmodels.formula.api</b></font>实际上是封装了ols的api，可以更便捷的调用"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "509bc7c3",
   "metadata": {},
   "source": [
    "### 使用pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b195c6ac",
   "metadata": {},
   "outputs": [
    {
     "ename": "TimeoutError",
     "evalue": "The read operation timed out",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTimeoutError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[13], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mstatsmodels\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01msm\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mstatsmodels\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mformula\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01msmf\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m data \u001b[38;5;241m=\u001b[39m sm\u001b[38;5;241m.\u001b[39mdatasets\u001b[38;5;241m.\u001b[39mget_rdataset(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGuerry\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHistData\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39mdata\n\u001b[1;32m      6\u001b[0m data\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m4\u001b[39m)\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/site-packages/statsmodels/datasets/utils.py:241\u001b[0m, in \u001b[0;36mget_rdataset\u001b[0;34m(dataname, package, cache)\u001b[0m\n\u001b[1;32m    238\u001b[0m data \u001b[38;5;241m=\u001b[39m read_csv(data, index_col\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m    239\u001b[0m data \u001b[38;5;241m=\u001b[39m _maybe_reset_index(data)\n\u001b[0;32m--> 241\u001b[0m title \u001b[38;5;241m=\u001b[39m _get_dataset_meta(dataname, package, cache)\n\u001b[1;32m    242\u001b[0m doc, _ \u001b[38;5;241m=\u001b[39m _get_data(docs_base_url, dataname, cache, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrst\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    244\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Dataset(data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;18m__doc__\u001b[39m\u001b[38;5;241m=\u001b[39mdoc\u001b[38;5;241m.\u001b[39mread(), package\u001b[38;5;241m=\u001b[39mpackage, title\u001b[38;5;241m=\u001b[39mtitle,\n\u001b[1;32m    245\u001b[0m                from_cache\u001b[38;5;241m=\u001b[39mfrom_cache)\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/site-packages/statsmodels/datasets/utils.py:182\u001b[0m, in \u001b[0;36m_get_dataset_meta\u001b[0;34m(dataname, package, cache)\u001b[0m\n\u001b[1;32m    177\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_dataset_meta\u001b[39m(dataname, package, cache):\n\u001b[1;32m    178\u001b[0m     \u001b[38;5;66;03m# get the index, you'll probably want this cached because you have\u001b[39;00m\n\u001b[1;32m    179\u001b[0m     \u001b[38;5;66;03m# to download info about all the data to get info about any of the data...\u001b[39;00m\n\u001b[1;32m    180\u001b[0m     index_url \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://raw.githubusercontent.com/vincentarelbundock/\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    181\u001b[0m                  \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRdatasets/master/datasets.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 182\u001b[0m     data, _ \u001b[38;5;241m=\u001b[39m _urlopen_cached(index_url, cache)\n\u001b[1;32m    183\u001b[0m     data \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstrict\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    184\u001b[0m     index \u001b[38;5;241m=\u001b[39m read_csv(StringIO(data))\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/site-packages/statsmodels/datasets/utils.py:157\u001b[0m, in \u001b[0;36m_urlopen_cached\u001b[0;34m(url, cache)\u001b[0m\n\u001b[1;32m    155\u001b[0m \u001b[38;5;66;03m# not using the cache or did not find it in cache\u001b[39;00m\n\u001b[1;32m    156\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m from_cache:\n\u001b[0;32m--> 157\u001b[0m     data \u001b[38;5;241m=\u001b[39m urlopen(url, timeout\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m)\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m    158\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m cache \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:  \u001b[38;5;66;03m# then put it in the cache\u001b[39;00m\n\u001b[1;32m    159\u001b[0m         _cache_it(data, cache_path)\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/urllib/request.py:216\u001b[0m, in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    215\u001b[0m     opener \u001b[38;5;241m=\u001b[39m _opener\n\u001b[0;32m--> 216\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m opener\u001b[38;5;241m.\u001b[39mopen(url, data, timeout)\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/urllib/request.py:519\u001b[0m, in \u001b[0;36mOpenerDirector.open\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m    516\u001b[0m     req \u001b[38;5;241m=\u001b[39m meth(req)\n\u001b[1;32m    518\u001b[0m sys\u001b[38;5;241m.\u001b[39maudit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124murllib.Request\u001b[39m\u001b[38;5;124m'\u001b[39m, req\u001b[38;5;241m.\u001b[39mfull_url, req\u001b[38;5;241m.\u001b[39mdata, req\u001b[38;5;241m.\u001b[39mheaders, req\u001b[38;5;241m.\u001b[39mget_method())\n\u001b[0;32m--> 519\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_open(req, data)\n\u001b[1;32m    521\u001b[0m \u001b[38;5;66;03m# post-process response\u001b[39;00m\n\u001b[1;32m    522\u001b[0m meth_name \u001b[38;5;241m=\u001b[39m protocol\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_response\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/urllib/request.py:536\u001b[0m, in \u001b[0;36mOpenerDirector._open\u001b[0;34m(self, req, data)\u001b[0m\n\u001b[1;32m    533\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[1;32m    535\u001b[0m protocol \u001b[38;5;241m=\u001b[39m req\u001b[38;5;241m.\u001b[39mtype\n\u001b[0;32m--> 536\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_chain(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandle_open, protocol, protocol \u001b[38;5;241m+\u001b[39m\n\u001b[1;32m    537\u001b[0m                           \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_open\u001b[39m\u001b[38;5;124m'\u001b[39m, req)\n\u001b[1;32m    538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result:\n\u001b[1;32m    539\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/urllib/request.py:496\u001b[0m, in \u001b[0;36mOpenerDirector._call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m    494\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m handler \u001b[38;5;129;01min\u001b[39;00m handlers:\n\u001b[1;32m    495\u001b[0m     func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(handler, meth_name)\n\u001b[0;32m--> 496\u001b[0m     result \u001b[38;5;241m=\u001b[39m func(\u001b[38;5;241m*\u001b[39margs)\n\u001b[1;32m    497\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    498\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/urllib/request.py:1391\u001b[0m, in \u001b[0;36mHTTPSHandler.https_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m   1390\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhttps_open\u001b[39m(\u001b[38;5;28mself\u001b[39m, req):\n\u001b[0;32m-> 1391\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdo_open(http\u001b[38;5;241m.\u001b[39mclient\u001b[38;5;241m.\u001b[39mHTTPSConnection, req,\n\u001b[1;32m   1392\u001b[0m         context\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_context, check_hostname\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_hostname)\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/urllib/request.py:1352\u001b[0m, in \u001b[0;36mAbstractHTTPHandler.do_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m   1350\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err: \u001b[38;5;66;03m# timeout error\u001b[39;00m\n\u001b[1;32m   1351\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m URLError(err)\n\u001b[0;32m-> 1352\u001b[0m     r \u001b[38;5;241m=\u001b[39m h\u001b[38;5;241m.\u001b[39mgetresponse()\n\u001b[1;32m   1353\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m   1354\u001b[0m     h\u001b[38;5;241m.\u001b[39mclose()\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/http/client.py:1386\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1384\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1385\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1386\u001b[0m         response\u001b[38;5;241m.\u001b[39mbegin()\n\u001b[1;32m   1387\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m:\n\u001b[1;32m   1388\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose()\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/http/client.py:325\u001b[0m, in \u001b[0;36mHTTPResponse.begin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    323\u001b[0m \u001b[38;5;66;03m# read until we get a non-100 response\u001b[39;00m\n\u001b[1;32m    324\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m--> 325\u001b[0m     version, status, reason \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_read_status()\n\u001b[1;32m    326\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m status \u001b[38;5;241m!=\u001b[39m CONTINUE:\n\u001b[1;32m    327\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/http/client.py:286\u001b[0m, in \u001b[0;36mHTTPResponse._read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    285\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_read_status\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 286\u001b[0m     line \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfp\u001b[38;5;241m.\u001b[39mreadline(_MAXLINE \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miso-8859-1\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    287\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(line) \u001b[38;5;241m>\u001b[39m _MAXLINE:\n\u001b[1;32m    288\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m LineTooLong(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstatus line\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/socket.py:706\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    704\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m    705\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 706\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sock\u001b[38;5;241m.\u001b[39mrecv_into(b)\n\u001b[1;32m    707\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m timeout:\n\u001b[1;32m    708\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_timeout_occurred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/ssl.py:1315\u001b[0m, in \u001b[0;36mSSLSocket.recv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1311\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m flags \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m   1312\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1313\u001b[0m           \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnon-zero flags not allowed in calls to recv_into() on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[1;32m   1314\u001b[0m           \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m)\n\u001b[0;32m-> 1315\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mread(nbytes, buffer)\n\u001b[1;32m   1316\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1317\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mrecv_into(buffer, nbytes, flags)\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.11/ssl.py:1167\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m   1165\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1166\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m buffer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1167\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sslobj\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;28mlen\u001b[39m, buffer)\n\u001b[1;32m   1168\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1169\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sslobj\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;28mlen\u001b[39m)\n",
      "\u001b[0;31mTimeoutError\u001b[0m: The read operation timed out"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "data = sm.datasets.get_rdataset('Guerry','HistData').data\n",
    "data.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c0e67297",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                Lottery   R-squared:                       0.348\n",
      "Model:                            OLS   Adj. R-squared:                  0.333\n",
      "Method:                 Least Squares   F-statistic:                     22.20\n",
      "Date:                Tue, 22 Mar 2022   Prob (F-statistic):           1.90e-08\n",
      "Time:                        11:16:10   Log-Likelihood:                -379.82\n",
      "No. Observations:                  86   AIC:                             765.6\n",
      "Df Residuals:                      83   BIC:                             773.0\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "Intercept         246.4341     35.233      6.995      0.000     176.358     316.510\n",
      "Literacy           -0.4889      0.128     -3.832      0.000      -0.743      -0.235\n",
      "np.log(Pop1831)   -31.3114      5.977     -5.239      0.000     -43.199     -19.424\n",
      "==============================================================================\n",
      "Omnibus:                        3.713   Durbin-Watson:                   2.019\n",
      "Prob(Omnibus):                  0.156   Jarque-Bera (JB):                3.394\n",
      "Skew:                          -0.487   Prob(JB):                        0.183\n",
      "Kurtosis:                       3.003   Cond. No.                         702.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "results = smf.ols('Lottery ~ Literacy + np.log(Pop1831)', data=data).fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46552df1",
   "metadata": {},
   "source": [
    "### 使用numpy arrays\n",
    "1. <font color=maroon><b>statsmodels.api</b></font>  下面的 <font color=maroon><b>OLS</b></font> 函数不会直接为回归模型添加常数项，因此我们需要手动使用<font color=maroon><b>sm.add_constant()</b></font> 添加常数列\n",
    "2. sm.OLS 进行最小二乘分析\n",
    "\n",
    "$$\n",
    "    y = \\beta_0 + \\beta_1 x_1 + \\beta_2 x_2 + \\epsilon\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6615377e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "\n",
    "nobs = 100\n",
    "X = np.random.random((nobs,2))\n",
    "X = sm.add_constant(X)\n",
    "beta = [1, .1, .5]\n",
    "epsilon = np.random.random(nobs)\n",
    "y = np.dot(X,beta) + epsilon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c3764a52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.110\n",
      "Model:                            OLS   Adj. R-squared:                  0.092\n",
      "Method:                 Least Squares   F-statistic:                     6.017\n",
      "Date:                Wed, 07 Aug 2024   Prob (F-statistic):            0.00344\n",
      "Time:                        01:28:29   Log-Likelihood:                -20.855\n",
      "No. Observations:                 100   AIC:                             47.71\n",
      "Df Residuals:                      97   BIC:                             55.53\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          1.5815      0.073     21.689      0.000       1.437       1.726\n",
      "x1             0.1260      0.115      1.094      0.277      -0.103       0.355\n",
      "x2             0.3375      0.106      3.180      0.002       0.127       0.548\n",
      "==============================================================================\n",
      "Omnibus:                       61.347   Durbin-Watson:                   1.843\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):                7.271\n",
      "Skew:                          -0.009   Prob(JB):                       0.0264\n",
      "Kurtosis:                       1.679   Cond. No.                         5.04\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "results = sm.OLS(y,X).fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "129b25ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "SSE=7.5369"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Latex\n",
    "beta = results.params\n",
    "df = results.df_resid\n",
    "SSE = np.dot(y.T,y) - np.dot(beta.T,np.dot(X.T,y))\n",
    "Latex(r'SSE=%.4f'%(SSE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "55b1187b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$s^2$=0.0777"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2 = SSE / df\n",
    "Latex(r'$s^2$=%.4f'%(s2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f0054ee",
   "metadata": {},
   "source": [
    "# 模型构建"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc30c3b3",
   "metadata": {},
   "source": [
    "## 电力负荷高阶多项式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "89ca645b",
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>workload</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>94</td>\n",
       "      <td>136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>96</td>\n",
       "      <td>131.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>95</td>\n",
       "      <td>140.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>108</td>\n",
       "      <td>189.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>67</td>\n",
       "      <td>96.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>88</td>\n",
       "      <td>116.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>89</td>\n",
       "      <td>118.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>84</td>\n",
       "      <td>113.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>90</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>106</td>\n",
       "      <td>178.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>67</td>\n",
       "      <td>101.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>71</td>\n",
       "      <td>92.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>100</td>\n",
       "      <td>151.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>79</td>\n",
       "      <td>106.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>97</td>\n",
       "      <td>153.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>98</td>\n",
       "      <td>150.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>87</td>\n",
       "      <td>114.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>76</td>\n",
       "      <td>100.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>68</td>\n",
       "      <td>96.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>92</td>\n",
       "      <td>135.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>100</td>\n",
       "      <td>143.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>85</td>\n",
       "      <td>111.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>89</td>\n",
       "      <td>116.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>74</td>\n",
       "      <td>103.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>86</td>\n",
       "      <td>105.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    temperature  workload\n",
       "0            94     136.0\n",
       "1            96     131.7\n",
       "2            95     140.7\n",
       "3           108     189.3\n",
       "4            67      96.5\n",
       "5            88     116.4\n",
       "6            89     118.5\n",
       "7            84     113.4\n",
       "8            90     132.0\n",
       "9           106     178.2\n",
       "10           67     101.6\n",
       "11           71      92.5\n",
       "12          100     151.9\n",
       "13           79     106.2\n",
       "14           97     153.2\n",
       "15           98     150.1\n",
       "16           87     114.7\n",
       "17           76     100.9\n",
       "18           68      96.3\n",
       "19           92     135.1\n",
       "20          100     143.6\n",
       "21           85     111.4\n",
       "22           89     116.5\n",
       "23           74     103.9\n",
       "24           86     105.1"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "temperature = [94,96,95,108,67,88,89,84,90,106,67,71,100,79,97,98,87,76,68,92,100,85,89,74,86]\n",
    "workload = [136.0,131.7,140.7,189.3,96.5,116.4,118.5,113.4,132.0,178.2,101.6,92.5,151.9,106.2,153.2,150.1,114.7,100.9,96.3,135.1,143.6,111.4,116.5,103.9,105.1]\n",
    "power_df = pd.DataFrame({'temperature': temperature,\n",
    "                         'workload': workload})\n",
    "power_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c107e2a1",
   "metadata": {},
   "source": [
    "### 绘制散点图预测趋势"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "fbc96f01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f95d3d2c130>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(10,6),\n",
    "                    dpi=80,\n",
    "                    facecolor='whitesmoke',\n",
    "                    edgecolor='grey')\n",
    "scatter = ax.scatter(power_df.temperature, power_df.workload, \n",
    "           label='temperature-workload', \n",
    "           c=power_df.temperature ,\n",
    "           cmap=plt.cm.autumn_r)\n",
    "ax.set_title('temperature on power workload scatter plot')\n",
    "ax.set_xlabel('temperature $^\\circ C$')\n",
    "ax.set_ylabel('power overload $kWh$')\n",
    "ax.legend()\n",
    "ax.grid(alpha=0.4)\n",
    "plt.colorbar(mappable=scatter, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd9fa8ac",
   "metadata": {},
   "source": [
    "### 构建三阶模型进行OLM回归\n",
    "$$\n",
    "    E(y) = \\beta_{0} + \\beta_{1}x + \\beta_{2}x^2 +  \\beta_{3}x^3\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9ac81874",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>workload</th>\n",
       "      <th>temperature2</th>\n",
       "      <th>temperature3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>94</td>\n",
       "      <td>136.0</td>\n",
       "      <td>8836</td>\n",
       "      <td>830584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>96</td>\n",
       "      <td>131.7</td>\n",
       "      <td>9216</td>\n",
       "      <td>884736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>95</td>\n",
       "      <td>140.7</td>\n",
       "      <td>9025</td>\n",
       "      <td>857375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>108</td>\n",
       "      <td>189.3</td>\n",
       "      <td>11664</td>\n",
       "      <td>1259712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>67</td>\n",
       "      <td>96.5</td>\n",
       "      <td>4489</td>\n",
       "      <td>300763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>88</td>\n",
       "      <td>116.4</td>\n",
       "      <td>7744</td>\n",
       "      <td>681472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>89</td>\n",
       "      <td>118.5</td>\n",
       "      <td>7921</td>\n",
       "      <td>704969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>84</td>\n",
       "      <td>113.4</td>\n",
       "      <td>7056</td>\n",
       "      <td>592704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>90</td>\n",
       "      <td>132.0</td>\n",
       "      <td>8100</td>\n",
       "      <td>729000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>106</td>\n",
       "      <td>178.2</td>\n",
       "      <td>11236</td>\n",
       "      <td>1191016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>67</td>\n",
       "      <td>101.6</td>\n",
       "      <td>4489</td>\n",
       "      <td>300763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>71</td>\n",
       "      <td>92.5</td>\n",
       "      <td>5041</td>\n",
       "      <td>357911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>100</td>\n",
       "      <td>151.9</td>\n",
       "      <td>10000</td>\n",
       "      <td>1000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>79</td>\n",
       "      <td>106.2</td>\n",
       "      <td>6241</td>\n",
       "      <td>493039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>97</td>\n",
       "      <td>153.2</td>\n",
       "      <td>9409</td>\n",
       "      <td>912673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>98</td>\n",
       "      <td>150.1</td>\n",
       "      <td>9604</td>\n",
       "      <td>941192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>87</td>\n",
       "      <td>114.7</td>\n",
       "      <td>7569</td>\n",
       "      <td>658503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>76</td>\n",
       "      <td>100.9</td>\n",
       "      <td>5776</td>\n",
       "      <td>438976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>68</td>\n",
       "      <td>96.3</td>\n",
       "      <td>4624</td>\n",
       "      <td>314432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>92</td>\n",
       "      <td>135.1</td>\n",
       "      <td>8464</td>\n",
       "      <td>778688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>100</td>\n",
       "      <td>143.6</td>\n",
       "      <td>10000</td>\n",
       "      <td>1000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>85</td>\n",
       "      <td>111.4</td>\n",
       "      <td>7225</td>\n",
       "      <td>614125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>89</td>\n",
       "      <td>116.5</td>\n",
       "      <td>7921</td>\n",
       "      <td>704969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>74</td>\n",
       "      <td>103.9</td>\n",
       "      <td>5476</td>\n",
       "      <td>405224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>86</td>\n",
       "      <td>105.1</td>\n",
       "      <td>7396</td>\n",
       "      <td>636056</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    temperature  workload  temperature2  temperature3\n",
       "0            94     136.0          8836        830584\n",
       "1            96     131.7          9216        884736\n",
       "2            95     140.7          9025        857375\n",
       "3           108     189.3         11664       1259712\n",
       "4            67      96.5          4489        300763\n",
       "5            88     116.4          7744        681472\n",
       "6            89     118.5          7921        704969\n",
       "7            84     113.4          7056        592704\n",
       "8            90     132.0          8100        729000\n",
       "9           106     178.2         11236       1191016\n",
       "10           67     101.6          4489        300763\n",
       "11           71      92.5          5041        357911\n",
       "12          100     151.9         10000       1000000\n",
       "13           79     106.2          6241        493039\n",
       "14           97     153.2          9409        912673\n",
       "15           98     150.1          9604        941192\n",
       "16           87     114.7          7569        658503\n",
       "17           76     100.9          5776        438976\n",
       "18           68      96.3          4624        314432\n",
       "19           92     135.1          8464        778688\n",
       "20          100     143.6         10000       1000000\n",
       "21           85     111.4          7225        614125\n",
       "22           89     116.5          7921        704969\n",
       "23           74     103.9          5476        405224\n",
       "24           86     105.1          7396        636056"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "power_df['temperature2'] = power_df.temperature**2\n",
    "power_df['temperature3'] = power_df.temperature**3\n",
    "power_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6571579e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               workload   R-squared:                       0.959\n",
      "Model:                            OLS   Adj. R-squared:                  0.954\n",
      "Method:                 Least Squares   F-statistic:                     165.4\n",
      "Date:                Tue, 22 Mar 2022   Prob (F-statistic):           9.14e-15\n",
      "Time:                        15:51:50   Log-Likelihood:                -75.917\n",
      "No. Observations:                  25   AIC:                             159.8\n",
      "Df Residuals:                      21   BIC:                             164.7\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "================================================================================\n",
      "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "Intercept      331.2527    477.111      0.694      0.495    -660.955    1323.460\n",
      "temperature     -6.3919     16.791     -0.381      0.707     -41.310      28.527\n",
      "temperature2     0.0378      0.195      0.194      0.848      -0.367       0.442\n",
      "temperature3  8.432e-05      0.001      0.114      0.911      -0.001       0.002\n",
      "==============================================================================\n",
      "Omnibus:                        0.221   Durbin-Watson:                   2.204\n",
      "Prob(Omnibus):                  0.895   Jarque-Bera (JB):                0.247\n",
      "Skew:                          -0.188   Prob(JB):                        0.884\n",
      "Kurtosis:                       2.690   Cond. No.                     3.26e+08\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 3.26e+08. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "results = smf.ols(formula='workload ~ temperature + temperature2 + temperature3', \n",
    "        data = power_df).fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "542ef3a4",
   "metadata": {},
   "source": [
    "### 修改后构建二建模型\n",
    "1. 发现temperature3这一项没有通过T假设检验，说明贡献量极小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4aa1ff44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               workload   R-squared:                       0.959\n",
      "Model:                            OLS   Adj. R-squared:                  0.956\n",
      "Method:                 Least Squares   F-statistic:                     259.7\n",
      "Date:                Tue, 22 Mar 2022   Prob (F-statistic):           4.99e-16\n",
      "Time:                        15:57:17   Log-Likelihood:                -75.925\n",
      "No. Observations:                  25   AIC:                             157.9\n",
      "Df Residuals:                      22   BIC:                             161.5\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "================================================================================\n",
      "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "Intercept      385.0481     55.172      6.979      0.000     270.627     499.469\n",
      "temperature     -8.2925      1.299     -6.384      0.000     -10.987      -5.598\n",
      "temperature2     0.0598      0.008      7.925      0.000       0.044       0.075\n",
      "==============================================================================\n",
      "Omnibus:                        0.283   Durbin-Watson:                   2.204\n",
      "Prob(Omnibus):                  0.868   Jarque-Bera (JB):                0.298\n",
      "Skew:                          -0.215   Prob(JB):                        0.862\n",
      "Kurtosis:                       2.683   Cond. No.                     4.12e+05\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 4.12e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "results = smf.ols(formula='workload ~ temperature + temperature2',\n",
    "        data=power_df).fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c1fca61",
   "metadata": {},
   "source": [
    "### 绘制预测图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "0e0ceff0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f95a29baf40>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V+Pzzzzlx4gTvvvsubdq0oUaNGvz1119YLBbc3NwYNWoUY8eOZfr06Tz99NPcc8893HLLLQQGBhIUFESZMmVYvnw5H3/8MQ8//DDJycn4+vry2WefXbOuOnXqMHz4cKZOnUpycnLmOFcRERFxctl7j6OioG1be1d0Ta4dVN3dITHRtKAWcEDN4Onpyeeff37F+bVr1+Y49vf3Z8eOHVe9x1133cWGDRuuOP/hhx/mOL7zzjuJiIjAy8srx/nIyMgrrq1SpQpr1qzJrXwRERFxNtkb5wqg99iWXDuohoSAv7/5l4R2sxIRERFnZOPeY1ty7aBarhy0a2fvKkRERERsoxB6j23JtYOqiIiIiDMr4r3HmvUvIiIi4qwyeo/d3Ni/HxYvtndBeaMWVREREREn9/ffcPfdcOSI2azq3/1/HJ5aVEVERESc2IUL0L27CamlSkHz5vauyHoKqiIiIiJOKjnZrO//229QrJjZUbVRI3tXZT0F1SJkypQpfPrpp5nH06dPZ/v27bleFxsbm+M6ERERcX7p6fDAA5CxbPrnn0OnTvatKa+cdoxqaqoZj2FrlSqBp51+is8++6xV78sIqvfff79tCxIRERGH8cwzsHCh+fytt2DgQPvWkx9OG1T//hv8/Gz/dWJioGrVa78+ZcoUfv75Z9LS0khISGDOnDls2bKFXbt2ceLECc6dO8eqVasAmDRpElFRUaSkpPDuu+/SokUL1q1bx+OPP065cuUAuP322zPvff/993P//ffTvn17AHbv3s3o0aNJSUmhUqVKzJ8/nw8//JDFixezd+9e2rdvz+DBg3nooYds9eMQERERB/DOOzBjhvn8ySdhwgT71pNf6vovBH5+fvz4449MmjSJZ555BoD58+czceLEzJC6atUqNm3axPr16wkNDWX8+PEAjBkzhjlz5vDDDz+QkpJy3a8zcuRIXnnlFTZs2EDHjh3Ztm0bEyZMYNGiRTRq1IjIyEiFVBERESe3aFFWMB08GN54w7713AinbVGtVMm0dhbG18lN83+n1911112Z3fWDBw+mUbbRzLt27eLw4cOZraPx8fEAHDlyJPN9rVq1uu7X2bNnT+bXGjNmDBaLJQ/fiYiIiBR1P/4Iw4ebzzt1grlzzeZURZXTBlVPz+t3yRemTZs28fDDD7Nt2zYCAgIAKF26dI731K9fnw4dOjBnzhzi4uJ47733ANMau2vXLurVq8fWrVupXbv2Nb9O7dq12bp1K61bt2by5MmUL1+eJ598Eh8fn8zga7FYcCuCO1OIiIjI9e3YAf/5D6SkmJn9S5eCl5e9q7oxThtUHcnJkyfp1KkTCQkJzJ07l02bNl3xnu7du7N27VqCgoKIj4/n4X9X4p05cybDhg2jXLlypKamXvfrzJ49mzFjxuDm5kaFChX4/PPPAbjlllsIDAwkKCiIMmXKsHz58oL/JkVERMRujhyBbt0gLg5uvx1WrYLL2sSKJLeLFy8Wyf7hgIAAjh07luNcWloa+/fvJyAgAA8PDztVltOUKVO4/fbbC23GfXJyMl4F9M8nR/x5yo2LiYnBrzBmGkqRpudEcqNnxHH88w+0bg379kH58rBhA/zbgWt3VapU4cCBA/m+Xi2qNjZlyhR7lyAiIiJOKiEBevY0IdXHB1audJyQWhCK8PBaEREREdeVmmrWRt20CTw8YPHiorU9qjWcKqhmTBLSbPeCkfFz1OQrERERx2KxwKOPwooV5jg01LSsOhun6vp3d3enWLFi/PPPP5QvX94lA1Z6ejppaWk3fB+LxcI///xDsWLFcC/K61qIiIg4oZdegtmzzedTp8KIEfatx1acKqgCVKtWjaNHj3L27Fl7l2IXqampeBbQnq7FihWjWrVqBXIvERERKRihoSaoAjzyCDz/vH3rsSWnC6peXl74+/uTnp7ukkMAjh8/TpUqVW74Pm5ubmpJFRERcTDLl5sufzBrpr7/PjhzB7LTBdUMrhqy3NzctJSUiIiIE9qwAQYMgPR0sxzVwoVmEpUzc800JyIiIlKE7N0LvXrBpUtQp45pWfXxsXdVtqegKiIiIuLA/voL7r4bzp6FKlUgIgJuusneVRUOBVURERERB3X+vNka9ehRKFMGwsPBleY5K6iKiIiIOKCkJDNh6vffwcsLli2DBg3sXVXhUlAVERERcTDp6TB8OERGmln98+dD+/b2rqrw2TyopqSkEBISQlRUFABfffUVnTp1IigoiJcyFgEDpkyZQuvWrQkJCeH06dO2LktERETEIVksMGGC2RIV4N13oV8/+9ZkLzZdnio5OZn+/ftz/PjxzHNTp07ll19+wcvLiw4dOjBw4ECOHDnC5s2bWbduHVFRUUydOpWZM2fasjQRERERhzRjhgmnAM88A2PH2rcee7J5i+qsWbNo3Lhx5rGvry9//PEHMTExxMXFceutt7J69WoGDBiAu7s7QUFBbNq0ydZliYiIiDic+fPh6afN58OHw2uv2bcee7Npi6qXlxeVK1fOcW7w4MEsWLCA4sWL07VrV0qVKkV8fDxVq1YFzIL1CQkJV9zrvffey9HKGhcXR0xMjC3LL5JcdetYsZ6eEbGGnhPJjZ6RghcV5c0DD1QE3AgKSuTFF89w7Ji9q7KvQt2Z6tChQ2zYsIEvvvgCgBdeeIFly5ZRqlSpHOE0OTn5imvHjRvHuHHjMo8DAgLw8/OzfdFFkH4ukhs9I2INPSeSGz0jBefXX+GRRyA1Fe66C7791gdfX/18C3XWf2JiIvv37ychIYH4+Hi2bt2Ku7s7zZs3Z+3atQAcOHAAX1/fwixLREREpPCcOwdRUWbWFHDokFkrNT4eataElStBUcgo1KBar149goODqVOnDv7+/lSqVInu3bvTo0cPNm7cyMSJExk2bBijR48uzLJERERECk9YGAQFQcOGnP5iNXffbeHUKahYEb77Dm65xd4FOo5C6foPDQ3N/HzatGlMmzYtx+vFihVjzZo1hIeH07dvX5o3b14YZYmIiIgUvvR08PEhftdhegwpw0GLGyWLp7JqpQc1a7rZuzqH4jAL/vv4+NCnTx+FVBEREXF68W6l6MFKtlqa4kkKS9JCCBzREFatyhwSIA4UVEVERERcQfwlT7pfWkoUQbiTxqfcT3DKt7BrF/ToAevX27tEh6GgKiIiIlJI4uOh+3t3sy69Ne6k8Rn3MYSF4O0NDRqYFtU2bexdpsMo1OWpRERERFxVfDx07w7rDtyKO2l8znCGeH8NAQ3g9dchOBjcNEY1OwVVERERERuLjzdLUK1fD+5u6XxuGc6QBjvh9TAF1OtQUBURERGxobg405K6fj24u8O8DxMYfMfD0LatAmouFFRFREREbOSKkDoPBg/2BdrZu7QiQUFVRERExAbi4kx3/88/Zw+p9q6qaFFQFRERESlgl4fU+fNh0CB7V1X0aHkqERERkQKkkFpw1KIqIiIiUkDi4swk/g0bTEhdsAAGDrR3VUWXWlRFRERECsCFCwqpBU0tqiIiIiI36MIF092fEVIXLoQBA+xdVdGnFlURERGRG5C9JdXDQyG1ICmoioiIiORTRkjduNGE1AULFFILkrr+RURERPLh8pC6cCH072/vqpyLgqqIiIhIHl24AHffDZs2KaTakrr+RURERPLg/PmcIfWLLxRSbUUtqiIiIiJWygipmzdnhdR+/exdlfNSUBURERGxwuUhddEi6NvX3lU5N3X9i4iIiORCIdU+1KIqIiIich3nz0PXrrBliwmpX34J995r76pcg1pURURERK4hNjYrpHp6KqQWNgVVERERkauIjTXd/RkhddEihdTCpq5/ERERkctktKRu3ZrVktqnj72rcj1qURURERHJRiHVcahFVURERORfl4fUxYshJMTeVbkuBVURERERTEjt0gW2bVNIdRTq+hcRERGXd+6cQqojUouqiIiIuLRz50x3f0ZI/eor+M9/7F2VgIKqiIiIuLCMltRfflFIdUQKqiIiIuKSLg+pS5ZA7972rkqy0xhVERERcTnZQ2qxYgqpjkotqiIiIuJSzp2Dzp3h119NSP3qK4VUR6UWVREREXEZZ8/mDKlqSXVsCqoiIiLiEs6eNd392UPqPffYuyq5HnX9i4iIiNPLaEn97TcTUr/+Gnr1sndVkhu1qIqIiIhTU0gtuhRURURExGkppBZt6voXERERp/TPPyakbt9uQurSpdCzp72rkrxQi6qIiIg4newh1ctLIbWoUlAVERERp3J5SP36a4XUokpd/yIiIuI0/vkHOnWCHTuyWlJ79LB3VZJfalEVERERp3DmjEKqs1GLqoiIiBR5Z86Y7v6MkBoWBt2727squVEKqiIiIlKkZbSk/v67QqqzUde/iIiIFFkKqc5NLaoiIiJSJF0eUpctg27d7F2VFCS1qIqIiEiRc+YMdOxoQqq3t0Kqs1JQFRERkSLl9GkTUnfuVEh1dgqqIiIiUmScPm26+7OH1OBge1cltqKgKiIiIkWCQqrr0WQqERERcXgZ3f27dpmQ+s03cPfd9q5KbE0tqiIiIuLQTp1SSHVVCqoiIiLisE6dMt39CqmuSV3/IiIi4pAyWlJ37zYhdfly6NrV3lVJYVKLqoiIiDic7CG1eHGFVFeloCoiIiIO5fKQ+s03CqmuSl3/IiIi4jBOnYIOHeCPP7JaUrt0sXdVYi9qURURERGHcPKkQqrkZPOgmpKSQkhICFFRUZnnEhMTadOmDQcOHMg8N2XKFFq3bk1ISAinT5+2dVkiIiLiQE6eNN39GSF1xQqFVLFxUE1OTqZfv34cO3Ysx/nnnnuOQYMGUatWLQC+++47Nm/ezLp163j88ceZOnWqLcsSERERB3K1kNq5s72rEkdg8xbVWbNm0bhx48zj7777jqVLl3Lp0iUiIyMBWL16NQMGDMDd3Z2goCA2bdpk67JERETEAfz9d87ufoVUyc6mk6m8vLyoXLlyjnMTJkzgzTffpFOnTjz99NPExMQQHx9P1apVAXBzcyMhIeGKe7333nvMnDkz8zguLo6YmBhbll8knT171t4liIPTMyLW0HMiuSmIZ+TUKXcGDryZgweL4e2dzpw5Z7jjjiT017tkKNRZ/2fOnCEtLY0BAwYA0L9/fxYtWsTNN9+cI5wmJydfce24ceMYN25c5nFAQAB+fn62L7oI0s9FcqNnRKyh50RycyPPyN9/w9ChcPAg+PjAihXudOp0cwFWJ86gUGf9V6hQAU9PT06ePAnAhg0bqFmzJs2bN2ft2rUAHDhwAF9f38IsS0RERApRRnf/3r0ZIdVskypyuUJfR3XGjBkMGDCA1NRUypQpw/z58/Hx8eGtt95i4sSJrFu3jtGjRxd2WSIiIlIITpwwE6cyQuq335pjkasplKAaGhqa+XlwcDDBwcFXvGfNmjWEh4fTt29fmjdvXhhliYiISCFSSJW8cpidqXx8fOjTp4+9yxAREREbOHHCdPfv26eQKtZzmKAqIiIizunykLpypTkWyY2CqoiIiNjM5SF11Spo397eVUlRUaiz/kVERMR1nDhhQum+fVCihEKq5J2CqoiIiBS4v/4yoXT/fhNSV65USJW8U9e/iIiIFKi//jLd/RkhddUqCAqyd1VSFKlFVURERAqMQqoUJKtaVP/++2+2b99OQkICt99+O02aNLF1XSIiIlLEZHT3HzigkCoF47otqidOnODRRx/l3XffJSkpiVKlSrFlyxaGDRvG8uXLC6tGERERcXDHj+cMqeHhCqly467Zonro0CEmT57M22+/za233pp5vkuXLjzyyCO89dZbvP/++zz22GOFUqiIiIg4puPHTXf/gQNQsqRpSW3Xzt5ViTO4ZlC97bbb+OSTTyhRosRVX3/yySe5cOGCzQoTERERx6eQKrZ0zaDq4eGRI6SGhYXx6aefkpycnHkuPDzcttWJiIiIw8ro7j940ITU8HBo29beVYkzsXp5qpdeeolPPvmEihUr2rIeERERKQJOnPBgyBCFVLEtq4Oqn58fjRo1wsPDw5b1iIiIiIM7dgwGDKjIkSMKqWJbuQbVBQsWANCwYUOCg4Pp379/5pCAIUOG2LY6ERERV3DuHOzcadKem5u9q7muvXuhWzc4cqQYJUtCRAS0aWPvqsRZ5brgf3R0NNHR0ZQsWZIOHTpw+vTpzHMiIiJSAMLCzFpODRua2UgWi70ruqr166FVKzhyBHx90xVSxeZybVFNTk6mRo0amR+VK1cujLpERERcR3o6+PjArl3Qpw/UqgWvv26aLh2khfWrr2DYMEhKgsqVYc6cU7RpU8neZYmTy7VFtXLlyhw8eJBPPvmEYcOGUalSJQIDA+nbt29h1CciIuIa3P/9KzkpKSuwOkgL6zvvwIABprT69WHTJqhbN8WuNYlryLVF9cyZMxw9epTTp0/j4+NDSEgItWrVIiAgoDDqExERcU0ZgbVHD4iKsstspbQ0ePJJePddc9yhcSxL15ShbDk3YmIKvRxxQbm2qB4/fpwTJ07g5uZGhQoV8PPzw8/Pj6pVqxZGfSIiIq7J2xsaNDAtqnYYCJqYCP37Z4XUIS0OEvHbzZRt928rr0ghyLVFtUePHsTExBATE8Ovv/5KWFgYPj4+VK9enY0bNxZGjSIiIs4vPd3819sbAgLMGNXgYLuMUT1zBnr3hg0bzPFzz8G06pG47fDMGpbQty8MHuxQ42jF+eQaVJcuXUrNmjWpV68evXr1onr16tx8882FUZuIiIhrcHc3TZgNGtg1oAIcOmS+/IEDpqxZs+Dhh4HZ5BxHGxPjsBO/xHnkGlRnz54NmGWqbrvtNsCsBPDmm28yefJk21YnIiLiCkJCwN/f7uuobt0KPXvCqVNQogR8+aU5viYHGEcrzi3XMaoZ7rvvPo4ePUpERATt2rXD29vblnWJiIi4jnLloF07u4bUb7+F9u1NSL35ZoiMzCWkgt3H0Yrzs3oL1dDQUHr37o2/vz/Lli2jUiWtnSYiImJXBbSj1YcfwpgxZphsQIDZErVGjau8Mfs4Wj8/ePZZuw5TEOeXa4vq+vXrWb9+PadOnWL8+PHs37+fnTt3sn79+sKoT0RERK7lBne0Sk83E6UefdR83qqVmUB11ZCafRxtWBi89prGpYrN5dqiOm/evBzHLVq0YMmSJbi5udFGzfwiIiL2cwM7WiUlwYgRsHChOb73Xpg3z9zuqi4fR6uFVKUQXDOoJiQkcPHiRT766KNrXnzkyBFuv/12W9QlIiIi1rjajla5BNbYWJM7IyPN8RNPwIwZWbe6qoxxtCKF6JqPZGxsLKNHj2bnzp1XvJaUlMRTTz3FhowF1kRERMQxZJ+Jf5VhekePmnlPkZEmw77zDrz9di4hVcROrtmiWrlyZebOncurr77KzJkzadiwISVKlODgwYMcP36cMWPG0KxZs8KsVURERHKTfcOAy4bobd9u8utff5m3LVhguvxFHNV1x6j6+vry6quvkpSUxIEDB7h06RLdu3fXjH8RERFHYeWOVj/8YEJpXBzcdBMsXw6tW9uhXpE8sGp5Km9vb+rXr2/rWkRERCQvrNzR6tNP4cEHITUVqlc3y0/dcUfhlyuSV1avoyoiIiIOJpcdrSwWePllePFFcxwYaBb2v+WWQq5TJJ8UVEVERIqq68zET0kx66N+8ok57tHDbIlasmQh1idygzTHT0RExMnExcE992SF1IcfhmXLFFKl6FFQFRERcSInTpjNqiIizPGrr8IHH4Cn+lClCNJjKyIi4iT27DFr/EdHQ7FiMGcODB1q76pE8k9BVURExAlERUHv3mbXqdKlISwMOna0d1UiN0Zd/yIiIkXcggXQpYsJqVWrmg2pFFLFGeTaolq3bl3crrLkBcDu3bsLvCARERGxTno6vPCCGYcK0LAhrFxpwqqIM8g1qP7xxx8AjB49mn79+tGqVSu2bNnC559/bvPiRERE5OouXoRhw0wXP0DPnrBwIZQqZd+6RAqS1V3/+/fvp0OHDnh7e9O2bVsOHz5sy7pERETkGmJioE2brJD61FNm+SmFVHE2Vk+mqlGjBoMGDaJJkyb8/vvvVKtWzZZ1iYiIyFVs3mwmTZ08aWb2f/ghjBhh76pEbMPqoPrRRx+xYsUKDh48SJ8+fejdu7ct6xIREZHLfPEFPPAAJCVB+fKwdOk1N6YScQpWd/1bLBaqVq1Ks2bNqFixIosXL7ZlXSIiIvKv9HT4739h8GATUuvWhS1bFFLF+Vndonrfffdx/PhxEhMTKV68OF5eXgwcONCWtYmIiLi8ixfhvvvg66/NcbdusGiRWStVxNlZ3aJ65swZVqxYQfXq1fnuu+9wd9cSrCIiIrZ0/LhpNc0IqU88AStW2CmknjtndhWwWOzwxcVVWZ02k5KS+PPPP0lMTOTkyZOcPXvWlnWJiIi4tK1boWlT+PVX8PSEjz+Gt98GDw87FRQWBkFBZrHWVavsVIS4GquD6owZM4iOjmbUqFF06dKFHj162LIuERERl7V4sWlJPXECbroJfvgBRo2yc1Hp6eDjA7t2QZ8+8NxzJrCqhVVsyOoxqk2aNKFevXocPXqUHTt24O3tbcu6REREXE56OkydCi+9ZI5r1zZd/f7+9q0rU8awv6Qks5hrnz5Qqxa8/roZPHuNnSxF8svqFtUff/yRxo0b8+CDD3LXXXfx008/2bIuERERlxIXB337ZoXUu++GTZscKKReTVKSaWHt0QPWr7d3NeKErG5RfeWVV1i7di0VK1bk9OnTDBgwgA4dOtiyNhEREZdw8KBZxP/fXcsZPx7efNOMTXVo3t4QEGBaVNu0sXc14oSs/l/AYrFQsWJFgMz/ioiIyI2JiIBBgyA21uS+0FAYPtzeVV1Derr5r7c3+PnBs89CcLC6/MVmrA6qnTt35j//+Q+tW7dm48aNdO7c2ZZ1iYiIODWLBd54w8xJsligalUzsT4w0N6VXYO7OyQmQoMGpgW1fn0TVkVsyOqgOnnyZNasWcPOnTsZM2YMnTp1smVdIiIiTuviRRg5Er780hy3aQNLlsAtt9i3rusKCTEDZtu2NS2oMTH2rkhcQJ5Gv3Tq1EkBVURE5AYcOQL/+Q/s2GGOH30U/vc/8PKyY1HWKFdOe7ZKodP2UiIiIpez0S5MP/5ouvZ37IBixcx41FmzikBIFbGTXFtUu3Xrds3XwsPDC7QYERERhxAWZvrm69cvkDVCLRZ491146ilIS4NKlcy2qK1aFWDNIk4o16D60UcfFUYdIiIijuPyXZhuYFH7xER45BH4/HNz3Lw5LF0KlSvboG4RJ5NrUK1WrVph1CEiIuJYsu/ClM/AmrF507Zt5njECNPVr80dRayT7zGqKSkpVr8vJCSEqKioHOd///13GjVqlHkcGhpKixYt6NatGwcPHsxvWSIiIraRx12Y1q0z41G3bTML97//PsyerZAqkhdWB9XPPvss8/M1a9bQtm3bXK9JTk6mX79+HDt2LMf5pKQkxo4dS1JSEgC7du0iNDSUn376iZkzZzJx4kRryxIRESkc3t5mDdFVq667C5PFAu+8Ax06wKlTULEirF4NY8ZoXXyRvLJ6eao1a9Zw7tw5fv31V9LT0/kyY/G3XMyaNYspU6bkOPfiiy8ydOhQ3n77bQAiIyPp1asXPj4++Pv7c+LECdLS0vDw8LD+OxERESlI2XdhytgmNJddmOLiYNQoWLzYHN91lxmPqlF0IvljdVCdO3cuo0aNoly5crz77rtWXePl5UXly0aLR0VFcfjwYaZPn54ZVOPj4/HLtrtFiRIlOH36NJUqVco899577zFz5szM47i4OGK02PAVzp49a+8SxMHpGRFruPxzUrw4NG1qdl4aOBDuvNOcv6yHMLsDBzx5+OEKHDxYDIDBg+OZMuWc066N7/LPiBSKXIPqww8/jNu//3r09PRkwYIFxMbG4uPjw4cffpinL3b+/HleeOEFlixZkuN8qVKluHjxYuZxamoqlsvWrhs3bhzjxo3LPA4ICMgRbiWLfi6SGz0jYg2Xfk569DDNoBm7MOVi8WIzUeriRZNxP/gA7r/fF/C1fa125NLPiBSKXIPq0KFDcxwPHz48318sMjKSxMREhg0bBsDJkyfp378/Tz/9NNOnT2fs2LGcP3+e6OhoKlasmO+vIyIickOs3IUpJQUmTjRrpALUqGHWR802V1hEbkCuQdWaSVPW6t27N7179848rlOnDov/HciTlJTE6NGj+fPPPxk2bBiennna3VVERKRQ/fUX9O8PP/9sjnv2NGullitn37pEnInVafDee+/l66+/ztcXCQ0Nver5PXv2ZH6+bNkywsPD8fHxoVOnTvn6OiIiIoUhMhIGDDCz+t3d4eWX4dlns5ZeFZGCYXVQLVOmDLt27aJ+/fo2KcTDw4OePXva5N4iIiIFwWKBGTPguefMVqgVKsAXX0DnzvauTMQ5WR1UK1WqRO/evenfvz+lS5cG4LnnnrNZYSIiIo7k/Hl44AEICzPHzZrBkiVmYQARsQ2rg2rdunV56aWXbFmLiIiIQ8rYQfXAAXM8ejS8/bZ2mRKxNauDasbs/+TkZLy8vGxWkIiIiCOZNw8eeQQSEsDHB0JD4bIFcUTERqwe9r1jxw4CAwOpXbs2TZs25ffff7dlXSIiIgXj3DmIijIDTPPg4kWzNurw4Sak+vvD5s0KqSKFyeqg+vTTTzN//nwOHTrE/PnzmThxoi3rEhERKRhhYRAUBA0bwqpVVgXW3bvNGNS5c81xv36wbRs0aGDjWkUkB6uDanp6OrVr1wbgjjvuID1jD2QRERFHlp5u+uwzBppeJ7BaLCacNm0Kf/xhxqDOmgVffgllytihdhEXZ3VQrVu3LmPHjuXLL7/k8ccfp27durasS0REnFU+u+JvSMYCp0lJ1wys8fFw332muz8xEWrVgk2b4NFHrdpFVURswOqg+s4779CkSRM2b95Mo0aNeOedd2xZl4iIOKt8dMUXuIzA2qMHrF/Pzp2mFXXePPPyoEHwyy/aClXE3qwOqmPHjsXd3Z1Ro0bxwAMP4K7tN0REJD/y0BVvM97e0KABlpWr+HhPG5o1g717oXhxM6t/wQIoVarwyhGRq7M6bYaEhHDu3Dlee+01qlWrRvfu3W1Zl4iIODMruuILVMa8in8DKmFhxK3fwZD53XjoYTcuXYI77jCz+h98UF39Io7C6nVUw8LC8PDwoHz58jzzzDPUqVPHlnWJiIgryd4VHxUFbdsW3L3d3c2g0wYN4PXXITiY37a70f8uOHjQvGXoUPjgA/D1LbgvKyI3zuqgWqVKFU6cOEFiYiJHjhyhePHitqxLRERcibc3BASYINmmTcHeOyTELILati0W3PjwQ3jiCZONfXzg/ffN1qhqRRVxPFYH1YoVK3Ly5EmOHDlCfHw8iYmJtqxLREScWfau+IyAGhxsm7RYrhy0a0dsLDz8MCxebE7XqQNffQX16hX8lxSRgmF1UL1w4QL33nsvTZo0wVd9IyIikl9X6Yq3dXNmZKTZYSomxhzff79pSS1Z0qZfVkRukNVB9cknn7RlHSIi4iqydcXbOqAmJcELL8CMGWaOVqlSJqAOH27TLysiBcTqoCoiIlIg/u2Kt7Vdu8wkqR07zHHr1mad1OrVbf6lRaSAaDFUERFxKunp8O67EBhoQqqnJ0ybBmvXKqSKFDVqURUREbOt6c6dhdIdb0vHj5sZ/D/8YI7vuMMs3n/XXfatS0TyRy2qIiJy5bamRdCSJWZ+VkZIHT0afv21kEPquXNmHVh7bAsr4oQUVEVE5MptTZ97rvC3Nc2nCxfgvvugXz+TE2+5BVauhP/7PyhRopCLuTzwF4Gfn4gjszqozp0715Z1iIiIvWXf1jQmxvbbmhaA9evhzjvh88/Nce/eZgSD3Xb5vjzwO/jPT8TRWR1U582bR1JSki1rERERR5J9W9P16+1dTQ7JyTB5smm8PHLErIc6e7Zp0KxY0c7FZQ/8CqwiN8TqoPrII4/wwAMPsHv3bmJiYojJWDVZRESck7e3GfS5alXBb2t6A/buhZYt4dVXTQNmixawfTuMHOmg88AcOPCLODqrZ/1ndP0/9dRTmefCw8MLviIREbGP7Nua+vnBs88Wyq5R1rJYYNYsmDjRbGzl4QH//S9MmmSWoHJY2beJdaDAL1IUWP2/tkKpiIgTu3xb0/r1TVh1EH//DSNGQMZfRf7+MH8+NG9u37quKnvgzwioDhT4RYqSPP0bdPXq1Rw/fpzatWtTo0YNKtp9IJCIiBSIy7c1daDhXcuWwYMPwpkz5vihh+Ctt8DX165lXd3lgV8BVeSGWB1Ux4wZw9mzZ4mOjub5559n+vTphIWF2bI2EREpLIW0rWlexMfD+PHwySfmuGJFM2HqnnvsWtb1XR74ReSGWD2Z6uDBg3zxxReUKVOG7t27Ex8fb8u6RETEhW3cCI0aZYXUHj3MslMOHVIhK/ArpIoUCKuDqru7Oz///DMAx44dw9vb22ZFiYiIa0pJgRdfNHOO/vzTLEn6wQewYoVZyF9EXIvVXf8zZsxg9OjR7N27l6FDhzJz5kxb1iUiIi7mwAEYOhS2bDHHgYFmwtQdd9i3LhGxH6uDalxcHN9//71aUkVEpEBZLPDxx/DEE5CQYOYjTZpklp4qVsze1YmIPVkdVD/88EP279+Pt7c3jRs3JjAwkMGDB9uyNhERcXKnTsGoUaZrH6B6ddOK2qqVfesSEcdg9RjV++67j/79+1O9enW2bdvGmjVrbFmXiIg4MYvFBNK6dbNC6gMPwI4dCqkiksXqFtUJEyZQv359goODGTduHLVr17ZlXSIi4qSio+GRRyAiwhyXLw+hodCnj33rEhHHY3WL6vr16xk6dCjff/897du357bbbrNlXSIi4mTS0uC996BevayQOmQI7NmjkCoiV2d1UA0KCmLp0qW0bduWn376iePHj9uyLhERcSK7d0Pr1vD443DxotmdddUq0/2vTQ5F5FqsDqrbtm2jS5cuxMTEcOjQITw987T7qoiIuKCkJLMuauPGsHmzWQd/3DgTXLt1s3d1IuLorE6bzz33HH///TetW7dm5cqVbN26lenTp9uyNhERKcI2bDAz+vfsMcd165otUFu2tG9dIlJ0WB1Ut27dyurVqwEYNWoUnTt3tllRIiJSdMXFwXPPwaxZZnZ/sWIweTI8+yxoKW4RyYs89d8fPXqUatWqERMTY6t6RESkCFu50szoP3bMHLdoYVpR69Wzb10iUjRZHVRffvllunfvjru7OxaLhdmzZ9uyLhERKUJOnYLx4+GLL8xxyZLw2mswejR4eNi1NBEpwqwOqi1btmTXrl2cOXOGChUq2LImEREpIjIW7h8/Hs6eNee6dYMPP4Rq1examog4Aatn/e/bt4/+/fsTHBzMoEGDOHDggC3rEhERB3fkCAQHw/DhJqRWqAALFpjuf4VUESkIVgfVRx99lPvvv58VK1YwZMgQHnnkEVvWJSIiDiotDf73PzPu9PvvzbmhQ83s/sGDzRJUIiIFweqgWrx4cbp3786tt95Kz549KV68uC3rEhERB7RzJ7RqBU88AQkJpuU0PBzmzTMtqiIiBcnqoOrh4cHo0aOZO3cujz76KMWLF2fBggW2rE1ERBxEUhK88AI0aQJbtuRcuD842N7ViYizytNkKoATJ07g5+eHn58fR48etVlhIiLiGNavhwcfhL17zXG9embJqRYt7FuXiDg/q4PqpEmTbFmHiIg4mAsXshbuB7Nw//PPm4X7vbzsW5uIuIY8LfgvIiKuYfXq4vz3v1kL97dqBR9/bLZBFREpLFaPURUREed34gQMHAgjRlTk2DHw9YX334d16xRSRaTwqUVVRERISYGZM2HKFIiLM+e6d4cPPtCaqCJiPwqqIiIuLjISHnvMzOAHqFQJJk/+hzFjymtNVBGxK3X9i4i4qOPHYdAg6NDBhFQPD5gwAfbtg969ExRSRcTu1KIqIuJiUlLg3XfhpZcgPt6ca9/ejEWtV88cnz9vt/JERDIpqIqIuJAffzTd/Hv2mOPKleGtt2DAAG19KiKOR13/IiIu4NgxE0Y7dTIh1dMTnnrKLOI/cKBCqog4JrWoiog4seRkeOcdePlluHjRnOvY0czw13JTIuLoFFRFRJzUDz/A2LFmchRAlSrw9tvQr59aUEWkaFDXv4iIk9m/H+65B7p2NSG1WDF45hnTzd+/v0KqiBQdNg+qKSkphISEEBUVBcDSpUsJCgqia9euPPjgg6SlpQEQGhpKixYt6NatGwcPHrR1WSIiTufcOXjiCTNzf8UKc65LF/j9d5g+3ewyJSJSlNi06z85OZn+/ftz/PhxAC5dusSiRYuIiIjAx8eHLl26sHnzZkqXLk1oaCjr1q3j+PHjTJw4kbCwMFuWJiLiNFJS4KOP4MUX4exZcy4gwHTzd++uFlQRKbps3qI6a9YsGjduDEDx4sVZvHgxPj4+pKen888//1CpUiUiIyPp1asXPj4++Pv7c+LEicyWVhERubaICLjzTjMW9exZKFcO/vc/2LULevRQSBWRos2mLapeXl5Urlz5qq/NnDmTli1bUqNGDeLj4/Hz88t8rUSJEpw+fZpKlSplnnvvvfeYOXNm5nFcXBwxMTG2K76IOpvRnCJyDXpGnMP+/Z688kpZIiN9APDwsDBsWDxPPHGBcuXS+fvvG7u/nhPJjZ4RKQx2mfW/Zs0avvnmG1auXAlAqVKluJixbgqQmpqKxWLJcc24ceMYN25c5nFAQECOcCtZ9HOR3OgZKbrOnIEpU+DDDyGj46l7d5gxw406dUrBuVTYuRPatr3h5lQ9J5IbPSNia4U+63/Tpk288MILLFq0CB8f0xLQvHlz1q5dC8D58+eJjo6mYsWKhV2aiIjDylgPtVYt+L//MyG1bl3T9b9yJdSp8+8bw8IgKAgaNoRVq+Cyf/SLiBQlhR5Uhw4dysWLFxk8eDCdO3cmPDycwMBAkpKSGD16NP3792fYsGF4emqJVxERiwW++Qbq14cJEyA2FsqXh1mzYMcOuPvuyy5ITwcfHzNItU8fBVYRKdIKJQ2GhoZmfn6tpaeWLVtGeHg4Pj4+dOrUqTDKEhFxaOvXm/VPN2wwx8WKmUlTL7wAZcte50L3f9sgkpKyAmutWvD669Ctm2ZYiUiR4TDNlh4eHvTs2dPeZYiI2N3u3TBpEixfnnUuJMTkzFq18nHDjMDaowdERZnxqyIiRYB2phIRcRAxMTBihOmtzwipbdvCxo2wdGk+QyqAtzc0aGCGALRpU2D1iojYmsO0qIqIuKqzZ83OUe+9Zxo/wYxJnT49nwv2p6eb/3p7m5X/X38dgoPV5S8iRY5aVEVE7CQxEd54A2rWhDffNCHVzw8+/RS2/3SOHqWicCOPk6ASE81H/fpmBYAdOzQuVUSKLAVVEZFClpoKc+aYrvxnnjEz+W+6Cd56C/bvh/vuA4/lBbDMlGb6i0gRp6AqIlJILBYz9vTOO2HkSDh+3Kwk9dxz8OefZvmp4sX/fXN+l5ny8dHyVCLiNBRURURszGKB77+Hli2hd2/44w+zgtSDD8KBA/Dqq9dYbupqy0xZEzzze52IiINRUBURsaGffoJ27czC/Js3m3P/+Y/Jj6GhUKVKHm6WfZmp9ettf52IiJ0pqIqI2MD69dChA3TsmJUNu3WDLVvMHKfMLU/zIr/LTGl5KhEporQ8lYhIAdq0Cf77X/jhh6xznYNSeKn/blo9emfeZt/nd5kpLU8lIk5CLaoiIgVg2zbTs96yZVZIbdcOIiPhh+HzaDWmcd7Gibq7m2WmGjTI2zJT+b1ORMQBqUVVROQGbN8OL76Yc7vTVq3g5ZdN17+bG3Dgshn8tWqZVs7rBciQEPD3N1tT5SVk5vc6EREHpBZVEZF82LUL+vaFxo2zQmqzZhARYcakdux4WU7M60z8cuVMk2xew2Z+rxMRcUAKqiJSdJ07B1FRhbrk0i+/wL33moz59dfmXOPGsGKFGZ96991WZkTNxBcRyZWCqogUXWEFsHuTldatM/ORAgNh6VLzpRo0MJ//8gv07JnHRkzNxBcRyZWCqogUXfndvclKFgt8953pSW/XznwOJqyGhZnxqSEhVgbU7DPxNdFJRMQqCqoiUrTZYBem9HTTUtq0qWlFXbfOnA8KMjtMbdliFu13t/Y3qGbii4jki2b9i4hzyT72MyrKzH63UmoqLFoEr71mtjnN0K0bTJ4MrVvnsybNxBcRyRcFVRFxLtkXubdy7GdSEnz2mbnk0CFzzs3NTJp67jlo0uQGa8qYiS8iInmioCoiRdsN7MIUFwcffwxvvQV//WXOeXjAkCHw7LP53OZUREQKjIKqiBRd2cd+5iGgxuyM5b1X4whdVZULF8z7vbxgxAh4+mmoXt3WhYuIiDUUVEWk6Mrj2M/ffjOtp18uKk1qWlkASvmk8NCjnkx40o3KlW1cr4iI5Ilm/YtI0WXFLkzp6bBypdkpqkkTWLAAUtPc8XOLYQZPEpNWhRnfN6TydtuuwyoiInmnoCoiTunSJZg9G+rXN4vx//STOd+kCSwc9SN/+jTgSd6mTPJpm63DKiIiN0ZBVUScypkzMHUq3HYbPPgg7NljzmeE1W3bYFDzQxRzS815obY0FRFxOBqjKiJOYdcu+L//M8tMJSaac97ecN998MQTULt2LjfIx7JWIiJiWwqqIlJknTgBCxfC/PlmO9MMFSrAmDEwejTcfPM1Lr6BZa1ERKRwKKiKSJESH2+2N50/H9asycqbAHXrwuOPw7Bh4ONznZvkc1krEREpXAqqIuLwUlNh9WqYNw+WLYOEhKzXbrkFBg+GoUOhcWMr86a2NBURKRIUVEXEIVks8OuvpuX0iy/g5Mms10qUMJP0hw6FTp3AM6+/ybSlqYhIkaCgKiIOJTrarHU6f37WjH0wvfWdO5tu/f/8B3x97VaiiIgUEgVVEbG72FhYssR07UdF5XytcWPTcjpoENx6q13KExERO1FQFRG7SE6G8HATTr/91ixjmsHPD4YMMQG1Xj371SgiIvaloCoihcZigY0bTbf+l1/C2bNZr5UuDf36mXDarp3p6hcREdemoCoiNnfggAmn8+fDoUNZ5z09oVs3M+60Z89clpQSERGXo6AqIjZx+rRpNZ0/HzZvzvlaixam5XTAALM4v4iIyNUoqIpIgUlMhBUrzLjTiAiz/mmGmjVNOB061CxhKiIikhsFVRG5IenpsHataTldsgQuXMh67aabYOBAE05btNDa+iIikjcKqiJFyblzsHOnQ+yotHu3aTldsACOHcs67+0NvXqZcafBweDlZb8aRUSkaFNQFSlKwsJg5EioX9/sUd+tW6EG1hMnzC5R8+bB9u05XwsKMuH03nuhbNlCK0lERJyYgqpIUZKebqbG79pl9hCtVcvmgTU+3uTjefNgzRpTQoY6dUw4HTIEqlWzyZcXEREXpqAqUtRkLDCalGSzwJqaCqtXm3GnYWGQkJD12i23wODBZtxp48Z2H4FQuBxo6IWIiCtQUBUp6jICa48eZv/Rtm3zdRuLBX791YTTL76AkyezXitRAkJCTOtpp05m/VOXZOehFyIirsZV/7oRcR7e3hAQYIJTmzZ5vjw62kyImj8f9uzJOu/uDp07m5bTkBDw9S3AmosqOwy9EBFxZQqqIkVNxiDR7AE1ODhPQSk21iwlNW+eaYTNrnFjE04HDYJbby24sp1GIQy9EBERQ0FVpChxdzer6jdokOeAmpwM4eEQGlqeNWtMzsrg52cmRA0dCvXq2ah2Z1VAQy9ERORKCqriuDRx5UohIWZbJyt/JhYLbNpkWk6//BLOngUoAUDp0tCvnwmn7dplNRRKHt3g0AsREbk2BVVxXJq4cqVy5UyqzMWBA2bM6fz5cOhQ1nlPT2jfPpGHHvKhZ08z3FLyqACGXoiIiHUUVMVxaeJKnpw5Y1pN582DzZtzvtaihWk5HTAAEhPP4OfnZ58ii7obGHohIiJ5p6Aqjk0TV64rMRFWrDAtp+HhZv3TDDVrmnA6dKgZLZAhJqbw63QaeRx6ISIiN0ZBVYoWTVzh77/Ntx4RAV9/DRcuZL12000wcKAJpy1aKEsVOCuHXoiISMFQUJWixQUnrkRHm2Ca8bF/f87Xvb2hVy+zGH9wMHh52adOERGRgqagKo7NxSauWCwmiGYPpkePXvm+0qWhdWszEqJvXyhbttBLFRERsTkF1evR8kj25QITV9LTzUiG7ME0+9alGcqXNz3OGR933gkeHoVfr4iISGFSUL0eLY9kX044cSUlBX77LSuUrltndom6XOXKEBSUFUxr19Y6pyIi4noUVK9HyyPZlxNMXLl0CbZuzQqmP/8MFy9e+b4aNXK2mNaooUdMREREQTU3Wh7JNRTQMI/4eNi4MSuYbt6cc6vSDHXrZoXStm2hatUbqF1ERMRJKajmlZZHck75HOZx7pxpJY2KgrVr4ZdfIC0t53vc3KBRo5zBtGJF23wbIiIizkRBNa9ccHkkl2DlMI+TJ8240owW099/NzP1s/P0hKZNs4Jpq1aalS8iIpIfCqq5cbHlkVzaVYZ5xNzWhqh7ZhB1/k7WRrmxb9+VlxUvbhbXz5j81Lw5lCxZuKWLiIg4I5sH1ZSUFPr3788TTzxBu3btOHDgACNHjsTT05OhQ4cyYsQIAEJDQ5kzZw7lypVj5syZ+Gff89FeXGB5JDEsFjhluZnd3M4u6vMLdxGV1I4j+6vDjJzvLVXKrGGa0WIaGGj+HSMiIiIFy6ZBNTk5mf79+3P8+PHMc6NGjWLq1KkEBQXRs2dPunTpwvnz5wkNDWXdunUcP36ciRMnEhYWZsvSrOOEyyMJnD4Nu3df9vHrUP5JGHXV999UKpl2nYrRrp1b5hqmnuqLEBERsTmb/3U7a9YspkyZAkBsbCx//fUX7du3B6BTp05ERUVx7tw5evXqhY+PD/7+/pw4cYK0tDQ87L2iuRMsj+TKzp69SiDdDadOXe3dxQEoRjJ3uO2nQemjtBlyG+0eqUvdel5aw1RERMQObBpUvby8qFy5cuZxfHw8fn5+mcdly5blxIkTpKam5jhfokQJTp8+TaVKlTLPvffee8ycOTPzOC4ujpiYGFuWXySdPXvW3iUUugsX3Ni/v9gVH6dOXfsfOh4eFqpXTyUgIMV8pO4gYONsqge4UWxwP9NsCsAxsnUIOAVXfEYk7/ScSG70jEhhKNQOzFKlSnEx22rnqampWCyWa57Pbty4cYwbNy7zOCAgIEe4lSzO+nOJi4M//riyhfTYsWtf4+4ONWuaVafq1cv6CAhww9u7GFDMvPFcA9g5ymWGeTjrMyIFS8+J5EbPiNhaoQbVMmXKAPD3339TqVIlNm7cSPfu3alevTrTp09n7NixnD9/nujoaCpqoUmnZ7GY7UNPn776x6lTOT//669r38vNDapXvzKQ1q5tZuXnSsM8REREHE6hTwkZP348/fr1o3nz5vz222/MnDkTX19fkpKSGD16NH/++SfDhg3DU7NVipz0dDMu9Fph8/KPM2cgNTXvX+e220wIzR5K69SBEiUK/nsSERER+ymUNBgaGpr5+aBBg6hfvz67d+/m+eefx9fXF4Bly5YRHh6Oj48PnTp1KoyyJBepqfDPP7kHzoyPf/7JWnY2r9zcoHx5s2PTtT5q1DCBtFSpgv0+RURExDHZpdmyQYMGNGjQIMc5Dw8PevbsaY9yrJKWZpZUTUyEhIQrP7/aucTEq+/zbksXLpSmdOm8XZOWlhVIs3/cyDh5Dw+oUOH6wTP7R/ny5hoRERGRDOpft8Lq1WZXzbg4e1dijTI2uWuxYtaHzptvNluGakknERERuREKqlYID79+SC1RwmwTn/GR/djbu3AnkV+6lEjx4j55usbNDW666dqhs2JFKF3aJSbDi4iIiANRUL2ec+dg50569WxLiRJuFC8OAwbkDKNXBNF/r7HXMkcxMWe0XIiIiIg4BXXOXk9YGAQF0X5cQ15uuYrJkyz4+0OVKmY1o+LFr5JF/72Ghg1h1SqzBpOIiIiI5JmC6vWkp5tm0127zCBVa8Jnfq4RERERkSsoqOYmY0ZQUpL14TM/14iIiIhIDgqqeZURPnv0gPXrbXeNiIiIiItTUM0rb29o0MC0jrZpY7trRERERFycZv3nJmOrJW9vCAiA11+H4ODrz+jPzzUiIiIikoOC6vW4u5vtpRo0sD5s5ucaEREREbmCgur1hISAv3/e1kTNzzUiIiIicgUF1espVw7atbP9NSIiIiJyBU2mEhERERGHpKAqIiIiIg5JQVVEREREHJKCqqs6dw6iorRTloiIiDgsBVVXFRYGQUHa2lVEREQcloKqq0pPBx8fs7Vrnz4KrCIiIuJwFFRdmfu/f/xJSQqsIiIi4nAUVCVLRmDt0QPWr7d3NSIiIuLiFFQli7e32fp11Spo08be1YiIiIiL085Uriw93fzX2xsCAuD11yE4WFu/ioiIiENQUHVV7u6QmGhaUBVQRURExAEpqLqqkBDw94e2bRVQRURExCEpqLqqcuWgXTt7VyEiIiJyTZpMJSIiIiIOSUFVRERERBySgqqIiIiIOCQFVRERERFxSAqqIiIiIuKQFFRFRERExCEpqIqIiIiIQ1JQFRERERGHpKAqIiIiIg5JQVVEREREHJKCqoiIiIg4JE97F5Bfp06dokqVKvYuw+HEx8fj6+tr7zLEgekZEWvoOZHc6BkRa5w8efKGri+yQTU2NtbeJTikWrVqceDAAXuXIQ5Mz4hYQ8+J5EbPiFijVq1aN3S9uv5FRERExCEpqIqIiIiIQ1JQdTJjx461dwni4PSMiDX0nEhu9IyINW70OXG7ePGipYBqEREREREpMGpRFRERERGHpKAqIiIiIg5JQbUImzNnDsHBwZkfFSpU4NixY3Tt2pVOnTrx8ssv27tEsbO4uDj69+9PcHAwnTt3Zu/evRw4cIB27drRsWNH5syZY+8Sxc5OnjzJ0KFD6datGz169GDv3r2cPn1av0ckU0pKCiEhIURFRQFc83dIaGgoLVq0oFu3bhw8eNBe5YodXP6MAERGRnLPPffkeN+UKVNo3bo1ISEhnD592qp7K6gWYSNGjCAiIoKIiAheeukl7rnnHiZNmsSQIUNYs2YN0dHRbN682d5lih0tWLCAFi1aEBERwdNPP81rr73GqFGjmDp1KmvWrOHrr78mJibG3mWKHb344ot0796d8PBwxo8fz4QJE3jyySf1e0QASE5Opl+/fhw7dizz3NV+h+zatYvQ0FB++uknZs6cycSJE+1YtRSmqz0jYWFhvPHGGyQnJ2ee++6779i8eTPr1q3j8ccfZ+rUqVbdX0HVSbz44ou8/PLLREZG0r9/fwC6devGjz/+aOfKxJ5uueUWdu7cycWLF9m2bRt16tThr7/+on379ri5udGpU6cc/wIW17Nnzx5atGgBwO23305sbKx+j0gOs2bNonHjxoDZbOdqv0MiIyPp1asXPj4++Pv7c+LECdLS0uxcuRSW7M8IQJMmTfjggw9yvGf16tUMGDAAd3d3goKC2LRpk1X3VlB1AqtXryYgIIAqVarg7e2Nj48PAGXKlOHEiRN2rk7sqXXr1ly6dIkPPviAnTt30rZtW/z8/DJfL1u2rJ4RFzdw4ECmTZvGxo0bmTRpEoMGDdLvEcnk5eVF5cqVM4/j4+Ov+jvk8vMlSpSwumtXirbLnxGA22677Yr3xcfHU7VqVQDc3NxISEiw6v5FdgtVyfLBBx/w4osvAuDpmfVHmpqaisWi1cdc2eTJk3nuuedo2LAhCQkJBAYGUqpUqczX9YzIo48+yu+//86KFSvYv38/8+fPZ9asWZmv6xmR7EqVKsXFixczjzOej2udF8lQqlSpHOE0+7CA61GLahF38uRJYmJiaNiwIQC1a9fmt99+A2DTpk1X/VeNuI6EhAS2b98OQFRUFL6+vgD8/fffAGzcuJHbb7/dTtWJo2jYsCEbN27knXfewdvbW79H5JrKlCkDXPk7pHnz5qxduxaA8+fPEx0dTcWKFe1Wpzie7M/IgQMHMv8+yo1aVIu4iIgI2rVrl3k8fvx4HnzwQe6++26++uorfvrpJztWJ/b2zDPPMHr0aCZMmEDlypV5++23OX36NP369aN58+b89ttvzJw5095lip19/fXX3HrrrXTs2BHQ7xG5vvHjx1/xO8TX15ekpCRGjx7Nn3/+ybBhw3L08In06NGDt956i4kTJ7Ju3TpGjx5t1XXamcoJHTp0iM2bN9O+fXtuvfVWe5cjDmjnzp3s3r2b4OBgypYta+9yxAHp94hcz9V+h6SlpREeHo6Pjw+dOnWyb4HikBITEwkPD6dKlSo0b97cqmsUVEVERETEIWmMqoiIiIg4JAVVEREREXFICqoiIiIi4pAUVEVERETEISmoioiIiIhDUlAVEXEh+/bty3G8cuVK7r33Xn755Zc830c7D4mIrSmoioi4iB07djB9+nTS0tIyz0VFRbFo0SLCwsKues2uXbsYOHAggYGBtGzZkqCgIJYvX87OnTuZNm1aYZUuIi5KQVVEnFZ0dDQrVqywdxlXsEddFouF1157jZkzZ+Lh4ZF5vkOHDgwaNIh77733imu2bt1KSEgI9913H9u2bWPjxo3MmTOH8+fP07dvXxITE/n9998L89sQERejoCoiTktBNcvmzZtp27btFftrBwcHs2TJEho3bpzjfGpqKiNGjGDq1Kl069Yt83zNmjUZNmwYYLbS/Oyzz2xfvIi4LAVVEXFKkydP5umnn+aHH34gODiYlStXkpKSwujRo7n77rvp3LkzBw4cAKBOnTo88sgj3HXXXYwZM4a6desyZswYevbsSbdu3Wjfvj179uwBuO49vv32W9q0aUN4eDgASUlJDBs2jLvvvpsOHTqwffv2q9YFMG/evBxd6XXq1MnxefZ7X6uG6/n1119p1aqV1T+/77//nkuXLtG/f/9rvqdixYrEx8dbfU8RkbxSUBURpzRt2jTeeOMNunTpQkREBD169GDu3LmkpaXx3XffMX78eF555ZXM97/44ovcdNNNDB8+nJYtW3Lo0CGqVq1KeHg4EydO5IUXXgC47j1+/PFHIiMjM1sgT5w4QZcuXQgPDyckJIRPP/30qnVZI/u9r1fDtSQmJlKiRAmrf36///47jRs3zjFM4Go0oUpEbMnT3gWIiBSWP/74g61btxIcHExqaiolS5bMfO3WW2/Fw8ODSpUq4eHhgcVioWnTpgA0btyY//73v7neY8qUKXh6Zv1a9fb2Zs2aNSxbtgxvb29KlSplVZ0JCQlXnMt+7+vVcC3VqlXjzz//5I477rCqBl9fX9LT06/7nvT0dAVVEbEptaiKiNPy8fHh4sWLgGn5q1u3Lv369SMiIoJPP/2U4ODg616/ZcsWwHSb+/v7A1z3HqVLl85x/ezZswkICGDp0qW0adPmmnUBeHl5ERcXB8C33357RS3Z753X7wOgS5cufP3117m+L0NwcDAbN25k69atmef27dvH8uXLM48jIiJyfF8iIgVNQVVEnFbjxo2xWCzcfffdjB07lvvvv5+jR4/SrVs3Bg4ciJ+f33WvP3XqFN27d+ett95i6tSpAHm6R9euXQkLC6NLly5s2bKFY8eOXbUugI4dO7JlyxYmTJhAdHT0devK6/cBULZsWerXr89XX32V63sB/P39WbBgAc8++yzNmjWjUaNGjB8/nvLlywNw8uRJPvvsM4YMGWLV/URE8sPt4sWL6rcREbnMtGnTqFatWuYMd2dgsVh4/fXXmThxYq5jT3Pz6aef0rlzZ6pWrVpA1YmIXElBVUREREQckrr+RURERMQhKaiKiIiIiENSUBURERERh6SgKiIiIiIOSUFVRERERBySgqqIiIiIOCQFVRERERFxSAqqIiIiIuKQFFRFRERExCH9P6cAM01ln1y9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parameters = results.params\n",
    "workload_ori = power_df[['temperature','temperature2']].values\n",
    "workload_cal = np.insert(workload_ori, 0, values=np.ones(len(workload_ori)), axis=1)\n",
    "workload_pred = np.dot(workload_cal,parameters)\n",
    "# 按照顺序进行排列\n",
    "workload_pred_plot = np.vstack((np.array(temperature),workload_pred))\n",
    "workload_pred_plot_sorted = np.sort(workload_pred_plot, axis=1)\n",
    "# 绘图\n",
    "fig,ax = plt.subplots(figsize=(10,6),dpi=80,\n",
    "                      facecolor='whitesmoke',\n",
    "                      edgecolor='grey')\n",
    "scatter = ax.scatter(power_df.temperature, power_df.workload,\n",
    "                     label='scatters', c='red', marker='>')\n",
    "line = ax.plot(workload_pred_plot_sorted[0], workload_pred_plot_sorted[1],\n",
    "               label='predict', lw=2, ls='-', color='blue')\n",
    "ax.set_title('original data and prediction line')\n",
    "ax.grid(alpha=0.4)\n",
    "ax.set_xlabel('temperature ($^\\circ C$)')\n",
    "ax.set_ylabel('power workload (kWh)')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e68f063",
   "metadata": {},
   "source": [
    "### 交换DataFrame中的两列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "8ab6d647",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>workload</th>\n",
       "      <th>temperature2</th>\n",
       "      <th>temperature3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>94</td>\n",
       "      <td>136.0</td>\n",
       "      <td>8836</td>\n",
       "      <td>830584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>96</td>\n",
       "      <td>131.7</td>\n",
       "      <td>9216</td>\n",
       "      <td>884736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>95</td>\n",
       "      <td>140.7</td>\n",
       "      <td>9025</td>\n",
       "      <td>857375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>108</td>\n",
       "      <td>189.3</td>\n",
       "      <td>11664</td>\n",
       "      <td>1259712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>67</td>\n",
       "      <td>96.5</td>\n",
       "      <td>4489</td>\n",
       "      <td>300763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   temperature  workload  temperature2  temperature3\n",
       "0           94     136.0          8836        830584\n",
       "1           96     131.7          9216        884736\n",
       "2           95     140.7          9025        857375\n",
       "3          108     189.3         11664       1259712\n",
       "4           67      96.5          4489        300763"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "power_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "9f01f31a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['temperature', 'temperature2', 'temperature3', 'workload']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>temperature2</th>\n",
       "      <th>temperature3</th>\n",
       "      <th>workload</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>94</td>\n",
       "      <td>8836</td>\n",
       "      <td>830584</td>\n",
       "      <td>136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>96</td>\n",
       "      <td>9216</td>\n",
       "      <td>884736</td>\n",
       "      <td>131.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>95</td>\n",
       "      <td>9025</td>\n",
       "      <td>857375</td>\n",
       "      <td>140.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>108</td>\n",
       "      <td>11664</td>\n",
       "      <td>1259712</td>\n",
       "      <td>189.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>67</td>\n",
       "      <td>4489</td>\n",
       "      <td>300763</td>\n",
       "      <td>96.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   temperature  temperature2  temperature3  workload\n",
       "0           94          8836        830584     136.0\n",
       "1           96          9216        884736     131.7\n",
       "2           95          9025        857375     140.7\n",
       "3          108         11664       1259712     189.3\n",
       "4           67          4489        300763      96.5"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = list(power_df)\n",
    "cols.insert(len(cols), cols.pop(cols.index('workload')))\n",
    "print(cols)\n",
    "power_df_new1 = power_df.loc[:, cols]\n",
    "power_df_new1.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "b25c1b17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>workload</th>\n",
       "      <th>temperature</th>\n",
       "      <th>temperature2</th>\n",
       "      <th>temperature3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>136.0</td>\n",
       "      <td>94</td>\n",
       "      <td>8836</td>\n",
       "      <td>830584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>131.7</td>\n",
       "      <td>96</td>\n",
       "      <td>9216</td>\n",
       "      <td>884736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>140.7</td>\n",
       "      <td>95</td>\n",
       "      <td>9025</td>\n",
       "      <td>857375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>189.3</td>\n",
       "      <td>108</td>\n",
       "      <td>11664</td>\n",
       "      <td>1259712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>96.5</td>\n",
       "      <td>67</td>\n",
       "      <td>4489</td>\n",
       "      <td>300763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   workload  temperature  temperature2  temperature3\n",
       "0     136.0           94          8836        830584\n",
       "1     131.7           96          9216        884736\n",
       "2     140.7           95          9025        857375\n",
       "3     189.3          108         11664       1259712\n",
       "4      96.5           67          4489        300763"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "workloads = power_df_new1['workload']\n",
    "power_df_new1.drop(labels=['workload'], axis=1, inplace=True)\n",
    "power_df_new1.insert(0, 'workload', workloads)\n",
    "power_df_new1.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5510ca05",
   "metadata": {},
   "source": [
    "### 对DataFrame中的列进行排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "c339bebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>workload</th>\n",
       "      <th>temperature</th>\n",
       "      <th>temperature2</th>\n",
       "      <th>temperature3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>96.5</td>\n",
       "      <td>67</td>\n",
       "      <td>4489</td>\n",
       "      <td>300763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101.6</td>\n",
       "      <td>67</td>\n",
       "      <td>4489</td>\n",
       "      <td>300763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>96.3</td>\n",
       "      <td>68</td>\n",
       "      <td>4624</td>\n",
       "      <td>314432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>92.5</td>\n",
       "      <td>71</td>\n",
       "      <td>5041</td>\n",
       "      <td>357911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>103.9</td>\n",
       "      <td>74</td>\n",
       "      <td>5476</td>\n",
       "      <td>405224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    workload  temperature  temperature2  temperature3\n",
       "4       96.5           67          4489        300763\n",
       "10     101.6           67          4489        300763\n",
       "18      96.3           68          4624        314432\n",
       "11      92.5           71          5041        357911\n",
       "23     103.9           74          5476        405224"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "power_df_sorted = power_df_new1.sort_values(by='temperature', axis=0, ascending=True)\n",
    "power_df_sorted.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffc134e7",
   "metadata": {},
   "source": [
    "## 产品质量完全二阶模型\n",
    "模型的最终公式为：\n",
    "\n",
    "$$\n",
    "    y = \\beta_0 + \\beta_1 x_\\psi + \\beta_2 x_\\phi + \\beta_3 x_\\psi x_\\phi + \\beta_4 {x_\\psi}^2 + \\beta_5 {x_\\phi}^2\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "68695af3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 80,  80,  80,  80,  80,  80,  80,  80,  80,  90,  90,  90,  90,\n",
       "        90,  90,  90,  90,  90, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "x_temp_80 = np.full((1,9),80)\n",
    "x_temp_90 = np.full((1,9),90)\n",
    "x_temp_100 = np.full((1,9),100)\n",
    "x_temp = np.hstack((x_temp_80,x_temp_90,x_temp_100)).reshape(-1)\n",
    "x_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "6893a2a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50, 50, 50, 55, 55, 55, 60, 60, 60, 50, 50, 50, 55, 55, 55, 60, 60,\n",
       "       60, 50, 50, 50, 55, 55, 55, 60, 60, 60])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_psi = np.array(list(50+(i%3)*5 for i in range(3)))\n",
    "x_psi_1 = np.vstack((x_psi,x_psi,x_psi)).flatten(order='F')\n",
    "x_psi =  np.hstack((x_psi_1,x_psi_1,x_psi_1))\n",
    "x_psi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "dcaf511a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50.8, 50.7, 49.4, 93.7, 90.9, 90.9, 74.5, 73. , 71.2, 63.4, 61.6,\n",
       "       63.4, 93.8, 92.1, 97.4, 70.9, 68.8, 71.3, 46.6, 49.1, 46.4, 69.8,\n",
       "       72.5, 73.2, 38.7, 42.5, 41.4])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_quality = np.array([50.8, 50.7, 49.4, 93.7, 90.9, 90.9, 74.5, 73.0, 71.2, \n",
    "             63.4, 61.6, 63.4, 93.8, 92.1, 97.4, 70.9, 68.8, 71.3,\n",
    "             46.6, 49.1, 46.4, 69.8, 72.5, 73.2, 38.7, 42.5, 41.4\n",
    "            ],dtype=np.float64)\n",
    "y_quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "998bfe72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x_temp</th>\n",
       "      <th>x_psi</th>\n",
       "      <th>y_quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80</td>\n",
       "      <td>50</td>\n",
       "      <td>50.8</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80</td>\n",
       "      <td>50</td>\n",
       "      <td>50.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80</td>\n",
       "      <td>50</td>\n",
       "      <td>49.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80</td>\n",
       "      <td>55</td>\n",
       "      <td>93.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>80</td>\n",
       "      <td>55</td>\n",
       "      <td>90.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x_temp  x_psi  y_quality\n",
       "0      80     50       50.8\n",
       "1      80     50       50.7\n",
       "2      80     50       49.4\n",
       "3      80     55       93.7\n",
       "4      80     55       90.9"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quality_df = pd.DataFrame({'x_temp': x_temp,\n",
    "                           'x_psi': x_psi,\n",
    "                           'y_quality': y_quality})\n",
    "quality_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "576f3956",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x_temp</th>\n",
       "      <th>x_psi</th>\n",
       "      <th>y_quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80</td>\n",
       "      <td>50</td>\n",
       "      <td>50.300000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80</td>\n",
       "      <td>55</td>\n",
       "      <td>91.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>72.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>90</td>\n",
       "      <td>50</td>\n",
       "      <td>62.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90</td>\n",
       "      <td>55</td>\n",
       "      <td>94.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>90</td>\n",
       "      <td>60</td>\n",
       "      <td>70.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100</td>\n",
       "      <td>50</td>\n",
       "      <td>47.366667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100</td>\n",
       "      <td>55</td>\n",
       "      <td>71.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>40.866667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x_temp  x_psi  y_quality\n",
       "0      80     50  50.300000\n",
       "1      80     55  91.833333\n",
       "2      80     60  72.900000\n",
       "3      90     50  62.800000\n",
       "4      90     55  94.433333\n",
       "5      90     60  70.333333\n",
       "6     100     50  47.366667\n",
       "7     100     55  71.833333\n",
       "8     100     60  40.866667"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quality_re_df = quality_df.groupby(['x_temp','x_psi']).mean().reset_index()\n",
    "quality_re_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e905962",
   "metadata": {},
   "source": [
    "### 原始数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "4f255197",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_quality</th>\n",
       "      <th>x_temp</th>\n",
       "      <th>x_psi</th>\n",
       "      <th>cross</th>\n",
       "      <th>temp2</th>\n",
       "      <th>psi2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50.300000</td>\n",
       "      <td>80</td>\n",
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       "      <td>4000</td>\n",
       "      <td>6400</td>\n",
       "      <td>2500</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91.833333</td>\n",
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       "      <td>6400</td>\n",
       "      <td>3025</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>72.900000</td>\n",
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       "      <td>4800</td>\n",
       "      <td>6400</td>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>62.800000</td>\n",
       "      <td>90</td>\n",
       "      <td>50</td>\n",
       "      <td>4500</td>\n",
       "      <td>8100</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>94.433333</td>\n",
       "      <td>90</td>\n",
       "      <td>55</td>\n",
       "      <td>4950</td>\n",
       "      <td>8100</td>\n",
       "      <td>3025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70.333333</td>\n",
       "      <td>90</td>\n",
       "      <td>60</td>\n",
       "      <td>5400</td>\n",
       "      <td>8100</td>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>47.366667</td>\n",
       "      <td>100</td>\n",
       "      <td>50</td>\n",
       "      <td>5000</td>\n",
       "      <td>10000</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>71.833333</td>\n",
       "      <td>100</td>\n",
       "      <td>55</td>\n",
       "      <td>5500</td>\n",
       "      <td>10000</td>\n",
       "      <td>3025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>40.866667</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>6000</td>\n",
       "      <td>10000</td>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   y_quality  x_temp  x_psi  cross  temp2  psi2\n",
       "0  50.300000      80     50   4000   6400  2500\n",
       "1  91.833333      80     55   4400   6400  3025\n",
       "2  72.900000      80     60   4800   6400  3600\n",
       "3  62.800000      90     50   4500   8100  2500\n",
       "4  94.433333      90     55   4950   8100  3025\n",
       "5  70.333333      90     60   5400   8100  3600\n",
       "6  47.366667     100     50   5000  10000  2500\n",
       "7  71.833333     100     55   5500  10000  3025\n",
       "8  40.866667     100     60   6000  10000  3600"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_quality = quality_re_df['y_quality']\n",
    "quality_re_df.drop(labels=['y_quality'],axis=1,inplace=True)\n",
    "quality_re_df.insert(0,'y_quality',y_quality)\n",
    "quality_re_df['cross'] = quality_re_df['x_temp'] * quality_re_df['x_psi']\n",
    "quality_re_df['temp2'] = quality_re_df['x_temp'] ** 2\n",
    "quality_re_df['psi2'] = quality_re_df['x_psi'] ** 2\n",
    "quality_re_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0876d60",
   "metadata": {},
   "source": [
    "### OLS分析\n",
    "#### 散点图查看关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "92ed0172",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(10,6),\n",
    "                       dpi=80,\n",
    "                       facecolor='whitesmoke',\n",
    "                       edgecolor='grey')\n",
    "ax1 = fig.add_subplot(1,1,1, projection='3d')\n",
    "\n",
    "quality_x1 = quality_re_df['x_temp'].values\n",
    "quality_x2 = quality_re_df['x_psi'].values\n",
    "quality_y = quality_re_df['y_quality'].values\n",
    "\n",
    "scatter = ax1.scatter(quality_x1,quality_x2,quality_y,\n",
    "            label='scatter',\n",
    "            c=quality_y,\n",
    "            cmap=plt.cm.autumn)\n",
    "ax1.set_title('scatter plot with quality')\n",
    "ax1.legend()\n",
    "ax1.grid(alpha=0.4)\n",
    "plt.colorbar(mappable=scatter, ax=ax1)\n",
    "for i in range(len(quality_y)):\n",
    "    ax1.text(quality_x1[i]*1.01, quality_x2[i]*1.01, quality_y[i]*1.05, \n",
    "             np.around(quality_y[i],decimals=2),\n",
    "             fontsize=8, color='orange', style='italic', weight='light', \n",
    "             verticalalignment='center', horizontalalignment='right', rotation=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b87fef79",
   "metadata": {},
   "source": [
    "#### 分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "af632d8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['y_quality', 'x_temp', 'x_psi', 'cross', 'temp2', 'psi2'], dtype='object')"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quality_re_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "8dddb879",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              y_quality   R-squared:                       0.999\n",
      "Model:                            OLS   Adj. R-squared:                  0.997\n",
      "Method:                 Least Squares   F-statistic:                     611.9\n",
      "Date:                Wed, 23 Mar 2022   Prob (F-statistic):           0.000104\n",
      "Time:                        10:25:01   Log-Likelihood:                -7.4288\n",
      "No. Observations:                   9   AIC:                             26.86\n",
      "Df Residuals:                       3   BIC:                             28.04\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept  -5127.8991    108.880    -47.097      0.000   -5474.405   -4781.393\n",
      "x_temp        31.0964      1.327     23.431      0.000      26.873      35.320\n",
      "x_psi        139.7472      3.100     45.083      0.000     129.882     149.612\n",
      "cross         -0.1455      0.010    -15.208      0.001      -0.176      -0.115\n",
      "temp2         -0.1334      0.007    -19.717      0.000      -0.155      -0.112\n",
      "psi2          -1.1442      0.027    -42.283      0.000      -1.230      -1.058\n",
      "==============================================================================\n",
      "Omnibus:                        0.744   Durbin-Watson:                   3.221\n",
      "Prob(Omnibus):                  0.689   Jarque-Bera (JB):                0.617\n",
      "Skew:                           0.492   Prob(JB):                        0.734\n",
      "Kurtosis:                       2.175   Cond. No.                     3.46e+06\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 3.46e+06. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/collinsliu/opt/anaconda3/lib/python3.9/site-packages/scipy/stats/stats.py:1541: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=9\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
     ]
    }
   ],
   "source": [
    "quality_ols = smf.ols(formula='y_quality ~ x_temp + x_psi + cross + temp2 + psi2',\n",
    "                  data=quality_re_df)\n",
    "results = quality_ols.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0e0c993",
   "metadata": {},
   "source": [
    "#### 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "a463fd57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    90.748148\n",
      "dtype: float64\n",
      "#####################\n",
      "[50.9287037  90.74814815 73.35648148 62.38148148 94.92592593 70.25925926\n",
      " 47.15648148 72.42592593 40.48425926]\n"
     ]
    }
   ],
   "source": [
    "# 构造数据\n",
    "new_quality_df = quality_re_df[['x_temp', 'x_psi', 'cross', 'temp2', 'psi2']]\n",
    "new_quality_df = sm.add_constant(new_quality_df)\n",
    "# 使用predict()函数预测\n",
    "new_y = results.predict(new_quality_df.loc[1])\n",
    "\n",
    "print(new_y)\n",
    "print('#####################')\n",
    "# 使用params进行手动预测\n",
    "params = results.params\n",
    "new_y = np.dot(new_quality_df,params)\n",
    "print(new_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "d600f4f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    50.928704\n",
       "1    90.748148\n",
       "2    73.356481\n",
       "3    62.381481\n",
       "4    94.925926\n",
       "5    70.259259\n",
       "6    47.156481\n",
       "7    72.425926\n",
       "8    40.484259\n",
       "dtype: float64"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_fitted = results.fittedvalues"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e04be0c",
   "metadata": {},
   "source": [
    "#### 绘制拟合平面图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "f34695dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9,) (9,) (9,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "\n",
    "y_plot = np.arange(45,65,.5)\n",
    "x_plot = np.arange(75,105,.5)\n",
    "yy, xx = np.meshgrid(y_plot, x_plot)\n",
    "\n",
    "x_ori = quality_re_df['x_temp'].values\n",
    "y_ori = quality_re_df['x_psi'].values\n",
    "\n",
    "fig = plt.figure(figsize=(8,10),\n",
    "                 dpi=80,\n",
    "                 facecolor='whitesmoke',\n",
    "                 edgecolor='grey')\n",
    "ax = plt.subplot(1,1,1,projection='3d')\n",
    "params = results.params\n",
    "zz_ori = quality_re_df['y_quality'].values\n",
    "zz_pred = results.fittedvalues\n",
    "zz_plane = params[0] + params[1] * xx + params[2] * yy \\\n",
    "         + params[3] * xx * yy + params[4] * xx * xx \\\n",
    "         + params[5] * yy * yy\n",
    "ax.set_title('fitting results')\n",
    "ax.set_xlabel(r'temperature $x_{1}$')\n",
    "ax.set_ylabel(r'pressure intensity $\\psi$ $x_{2}$')\n",
    "ax.set_zlabel(r'estimation value $y$')\n",
    "\n",
    "surface = ax.plot_surface(xx,yy,zz_plane,color='cyan',alpha=0.6)\n",
    "scatter_ori = ax.scatter(x_ori,y_ori,zz_ori, color='red', label='original values')\n",
    "scatter_pred = ax.scatter(x_ori,y_ori,zz_pred, color='black', label='predicted values')\n",
    "\n",
    "print(x_ori.shape,y_ori.shape,zz_ori.shape)\n",
    "for i in range(len(zz_pred)):\n",
    "    ax.text(x_ori[i]*1.01, y_ori[i]*1.01, zz_ori[i]*1.05,\n",
    "            np.around(zz_ori[i], decimals=2), fontsize=8, color='red', style='italic', weight='bold',\n",
    "            verticalalignment='center', horizontalalignment='right', rotation=0)\n",
    "\n",
    "fake2Dline = mpl.lines.Line2D([0],[0], linestyle=\"none\", c='cyan', marker = 'o')\n",
    "ax.legend([fake2Dline], ['Lyapunov function on XY plane'], numpoints = 1)\n",
    "\n",
    "ax.legend()\n",
    "ax.grid(alpha=0.4)\n",
    "ax.view_init(60,35)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c664410d",
   "metadata": {},
   "source": [
    "## 官网实例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "10755f3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.62982808,  5.01523585,  5.1342375 ,  5.91179481,  6.20668629,\n",
       "        5.95962945,  6.34482413,  6.21824907,  6.10828042,  6.5777078 ,\n",
       "        6.49867398,  6.65596853,  6.89833083,  7.68965434,  7.48410451,\n",
       "        8.4944944 ,  8.00557793,  9.03008942,  9.02590176,  9.1397801 ,\n",
       "        9.47007431,  9.54279075,  9.45185205,  9.47620688,  9.49620348,\n",
       "        9.29655139,  9.60663799,  9.36771806,  9.12810721,  9.58818912,\n",
       "        9.97045787, 10.30250499, 10.38018596, 10.57751566, 11.19476696,\n",
       "       11.34861292, 10.69389028, 10.92372203, 10.54780471, 10.92523197,\n",
       "       10.15725669, 10.35776756,  9.73298207, 10.45445076, 10.16447298,\n",
       "       10.65346737, 10.0935959 , 10.7876946 , 10.7115556 , 10.61160153])"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nsample = 50\n",
    "sig = 0.25\n",
    "x1 = np.linspace(0, 20, nsample)\n",
    "X = np.column_stack((x1, np.sin(x1), (x1 - 5) ** 2))\n",
    "X = sm.add_constant(X)\n",
    "beta = [5.0, 0.5, 0.5, -0.02]\n",
    "y_true = np.dot(X, beta)\n",
    "y = y_true + sig * np.random.normal(size=nsample)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "da6e2267",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.982\n",
      "Model:                            OLS   Adj. R-squared:                  0.981\n",
      "Method:                 Least Squares   F-statistic:                     859.2\n",
      "Date:                Wed, 23 Mar 2022   Prob (F-statistic):           2.21e-40\n",
      "Time:                        11:21:25   Log-Likelihood:               -0.62500\n",
      "No. Observations:                  50   AIC:                             9.250\n",
      "Df Residuals:                      46   BIC:                             16.90\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          5.0598      0.087     58.114      0.000       4.885       5.235\n",
      "x1             0.4892      0.013     36.429      0.000       0.462       0.516\n",
      "x2             0.4526      0.053      8.574      0.000       0.346       0.559\n",
      "x3            -0.0189      0.001    -16.036      0.000      -0.021      -0.017\n",
      "==============================================================================\n",
      "Omnibus:                        1.368   Durbin-Watson:                   2.484\n",
      "Prob(Omnibus):                  0.505   Jarque-Bera (JB):                1.379\n",
      "Skew:                           0.346   Prob(JB):                        0.502\n",
      "Kurtosis:                       2.572   Cond. No.                         221.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "olsmod = sm.OLS(y, X)\n",
    "olsres = olsmod.fit()\n",
    "print(olsres.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "700a2cb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 4.58710978  5.04042559  5.45792812  5.81495266  6.09573588  6.29600567\n",
      "  6.42368305  6.49758078  6.54431243  6.59391991  6.67493763  6.80970436\n",
      "  7.0106931   7.27846172  7.60156136  7.95841744  8.32087427  8.65882097\n",
      "  8.94513863  9.16015581  9.29488056  9.35247773  9.34774902  9.30470109\n",
      "  9.25260134  9.22116937  9.23569409  9.31287887  9.45809722  9.6645099\n",
      "  9.91418864 10.18106165 10.43519718 10.64772122 10.7955614  10.86523771\n",
      " 10.85507727 10.77548911 10.64725408 10.49811054 10.35819707 10.25510077\n",
      " 10.20932478 10.23091968 10.3178322  10.45624255 10.62283502 10.78862864\n",
      " 10.92373868 11.00228665]\n"
     ]
    }
   ],
   "source": [
    "ypred = olsres.predict(X)\n",
    "print(ypred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6005ff9",
   "metadata": {},
   "source": [
    "## 昆虫采集编码二阶模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "9a568a54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.8</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.5</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.7</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.6</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>22.6</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23.3</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18.2</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18.6</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   temperature  ratio\n",
       "0         16.8   0.66\n",
       "1         15.0   0.30\n",
       "2         16.5   0.46\n",
       "3         17.7   0.44\n",
       "4         20.6   0.67\n",
       "5         22.6   0.99\n",
       "6         23.3   0.75\n",
       "7         18.2   0.24\n",
       "8         18.6   0.51"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.api as sm\n",
    "\n",
    "insect_temperature = [16.8, 15.0, 16.5, 17.7, 20.6, 22.6, 23.3, 18.2, 18.6]\n",
    "insect_ratio = [0.66, 0.30, 0.46, 0.44, 0.67, 0.99, 0.75, 0.24, 0.51]\n",
    "\n",
    "insect_df = pd.DataFrame({'temperature': insect_temperature,\n",
    "                          'ratio': insect_ratio})\n",
    "insect_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "fb4067cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18.811111</td>\n",
       "      <td>0.557778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.812225</td>\n",
       "      <td>0.234509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16.800000</td>\n",
       "      <td>0.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18.200000</td>\n",
       "      <td>0.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>20.600000</td>\n",
       "      <td>0.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>23.300000</td>\n",
       "      <td>0.990000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       temperature     ratio\n",
       "count     9.000000  9.000000\n",
       "mean     18.811111  0.557778\n",
       "std       2.812225  0.234509\n",
       "min      15.000000  0.240000\n",
       "25%      16.800000  0.440000\n",
       "50%      18.200000  0.510000\n",
       "75%      20.600000  0.670000\n",
       "max      23.300000  0.990000"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insect_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82cfde9e",
   "metadata": {},
   "source": [
    "### 编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "02224f02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.715132</td>\n",
       "      <td>0.435899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.355194</td>\n",
       "      <td>-1.099224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.821809</td>\n",
       "      <td>-0.416947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.395100</td>\n",
       "      <td>-0.502231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.636112</td>\n",
       "      <td>0.478541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.347292</td>\n",
       "      <td>1.843095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.596205</td>\n",
       "      <td>0.819680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.217305</td>\n",
       "      <td>-1.355077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.075069</td>\n",
       "      <td>-0.203735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   temperature     ratio\n",
       "0    -0.715132  0.435899\n",
       "1    -1.355194 -1.099224\n",
       "2    -0.821809 -0.416947\n",
       "3    -0.395100 -0.502231\n",
       "4     0.636112  0.478541\n",
       "5     1.347292  1.843095\n",
       "6     1.596205  0.819680\n",
       "7    -0.217305 -1.355077\n",
       "8    -0.075069 -0.203735"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def normalization(X, mean, std):\n",
    "    return np.divide(X-mean, std)\n",
    "\n",
    "u_value = normalization(insect_df, insect_df.mean(), insect_df.std())\n",
    "u_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "58cb088f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f95b3ce6b50>"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,6),dpi=80,\n",
    "                 facecolor='whitesmoke',\n",
    "                 edgecolor='grey')\n",
    "ax = plt.subplot(1,1,1)\n",
    "scatter = ax.scatter(u_value['temperature'].values, u_value['ratio'].values, \n",
    "                     color='red', marker='o', label='original data')\n",
    "ax.grid(alpha=0.4)\n",
    "ax.set_xlabel('temperature')\n",
    "ax.set_ylabel('caption ratio')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5c0e265",
   "metadata": {},
   "source": [
    "### 确定模型并拟合\n",
    "采用二阶模型进行拟合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "ba1cfceb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['temperature', 'ratio'], dtype='object')"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insect_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "182879dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ratio</th>\n",
       "      <th>temperature</th>\n",
       "      <th>temperature2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.66</td>\n",
       "      <td>16.8</td>\n",
       "      <td>282.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.30</td>\n",
       "      <td>15.0</td>\n",
       "      <td>225.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.46</td>\n",
       "      <td>16.5</td>\n",
       "      <td>272.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.44</td>\n",
       "      <td>17.7</td>\n",
       "      <td>313.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.67</td>\n",
       "      <td>20.6</td>\n",
       "      <td>424.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.99</td>\n",
       "      <td>22.6</td>\n",
       "      <td>510.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.75</td>\n",
       "      <td>23.3</td>\n",
       "      <td>542.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.24</td>\n",
       "      <td>18.2</td>\n",
       "      <td>331.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.51</td>\n",
       "      <td>18.6</td>\n",
       "      <td>345.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ratio  temperature  temperature2\n",
       "0   0.66         16.8        282.24\n",
       "1   0.30         15.0        225.00\n",
       "2   0.46         16.5        272.25\n",
       "3   0.44         17.7        313.29\n",
       "4   0.67         20.6        424.36\n",
       "5   0.99         22.6        510.76\n",
       "6   0.75         23.3        542.89\n",
       "7   0.24         18.2        331.24\n",
       "8   0.51         18.6        345.96"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratio = insect_df['ratio']\n",
    "insect_df.drop(labels=['ratio'], axis=1, inplace=True)\n",
    "insect_df.insert(0, 'ratio', ratio)\n",
    "insect_df['temperature2'] = insect_df['temperature'] ** 2\n",
    "insect_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "9a09b0ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  ratio   R-squared:                       0.604\n",
      "Model:                            OLS   Adj. R-squared:                  0.472\n",
      "Method:                 Least Squares   F-statistic:                     4.571\n",
      "Date:                Wed, 23 Mar 2022   Prob (F-statistic):             0.0622\n",
      "Time:                        17:15:18   Log-Likelihood:                 4.9779\n",
      "No. Observations:                   9   AIC:                            -3.956\n",
      "Df Residuals:                       6   BIC:                            -3.364\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "================================================================================\n",
      "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "Intercept        1.0911      3.380      0.323      0.758      -7.179       9.361\n",
      "temperature     -0.1186      0.354     -0.335      0.749      -0.984       0.747\n",
      "temperature2     0.0047      0.009      0.517      0.624      -0.018       0.027\n",
      "==============================================================================\n",
      "Omnibus:                        0.060   Durbin-Watson:                   1.761\n",
      "Prob(Omnibus):                  0.970   Jarque-Bera (JB):                0.118\n",
      "Skew:                          -0.002   Prob(JB):                        0.943\n",
      "Kurtosis:                       2.440   Cond. No.                     2.25e+04\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.25e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/collinsliu/opt/anaconda3/lib/python3.9/site-packages/scipy/stats/stats.py:1541: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=9\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
     ]
    }
   ],
   "source": [
    "results = smf.ols(formula='ratio ~ temperature + temperature2', data=insect_df).fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "b07d86da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.0911095 , -0.11862446,  0.00470541])"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.params.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4814fa4",
   "metadata": {},
   "source": [
    "### 计算皮尔逊相关系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "41644f5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.39955556e-01, 4.03922222e+00, 1.58282656e+02],\n",
       "       [4.03922222e+00, 6.32688889e+01, 2.45344222e+03],\n",
       "       [1.58282656e+02, 2.45344222e+03, 9.54902094e+04]])"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def Pearson_coef(X):\n",
    "    mean = X.mean()\n",
    "    return np.dot((X-mean).T,X-mean)\n",
    " \n",
    "res = Pearson_coef(insect_df)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "b41c3944",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9981626346741037"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "res[2,1]/math.sqrt(res[1,1]*res[2,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "ab3a3dd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[8.         6.12474804]\n",
      " [6.12474804 8.        ]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.765593504581384"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "res_norm = Pearson_coef(u_value)\n",
    "print(res_norm)\n",
    "res_norm[1,0]/math.sqrt(res_norm[0,0]*res_norm[1,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "719eaaf3",
   "metadata": {},
   "source": [
    "## 一元定量自变量费用模型\n",
    "1. 定量自变量的数据转换需要清晰\n",
    "2. 另外采用的全局有效性F检验与ANOVA在形式上是一致的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "5b8497d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.api as sm\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "\n",
    "cost_x1_pri = [0,1,0]\n",
    "cost_x2_pri = [0,0,1]\n",
    "cost_y = np.array([198,563,385,126,314,693,443,483,266,570,144,586,\n",
    "          286,585,178,184,377,773,105,264,308,216,185,430,\n",
    "          465,330,644,203,354,515])\n",
    "cost_x1 = np.array(cost_x1_pri * 10)\n",
    "cost_x2 = np.array(cost_x2_pri * 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "627107ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cost</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>198</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>563</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>385</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>126</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>314</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>693</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>443</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>483</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>266</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>570</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>586</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>585</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>184</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>377</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>773</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>264</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>308</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>216</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>185</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>430</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>465</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>644</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>354</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>515</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    cost  x1  x2\n",
       "0    198   0   0\n",
       "1    563   1   0\n",
       "2    385   0   1\n",
       "3    126   0   0\n",
       "4    314   1   0\n",
       "5    693   0   1\n",
       "6    443   0   0\n",
       "7    483   1   0\n",
       "8    266   0   1\n",
       "9    570   0   0\n",
       "10   144   1   0\n",
       "11   586   0   1\n",
       "12   286   0   0\n",
       "13   585   1   0\n",
       "14   178   0   1\n",
       "15   184   0   0\n",
       "16   377   1   0\n",
       "17   773   0   1\n",
       "18   105   0   0\n",
       "19   264   1   0\n",
       "20   308   0   1\n",
       "21   216   0   0\n",
       "22   185   1   0\n",
       "23   430   0   1\n",
       "24   465   0   0\n",
       "25   330   1   0\n",
       "26   644   0   1\n",
       "27   203   0   0\n",
       "28   354   1   0\n",
       "29   515   0   1"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost_df = pd.DataFrame({'cost':cost_y,\n",
    "                        'x1':cost_x1,\n",
    "                        'x2':cost_x2})\n",
    "cost_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "db74d494",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   cost   R-squared:                       0.205\n",
      "Model:                            OLS   Adj. R-squared:                  0.146\n",
      "Method:                 Least Squares   F-statistic:                     3.482\n",
      "Date:                Thu, 24 Mar 2022   Prob (F-statistic):             0.0452\n",
      "Time:                        17:03:47   Log-Likelihood:                -194.88\n",
      "No. Observations:                  30   AIC:                             395.8\n",
      "Df Residuals:                      27   BIC:                             400.0\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    279.6000     53.426      5.233      0.000     169.979     389.221\n",
      "x1            80.3000     75.556      1.063      0.297     -74.728     235.328\n",
      "x2           198.2000     75.556      2.623      0.014      43.172     353.228\n",
      "==============================================================================\n",
      "Omnibus:                        2.611   Durbin-Watson:                   2.498\n",
      "Prob(Omnibus):                  0.271   Jarque-Bera (JB):                1.469\n",
      "Skew:                           0.233   Prob(JB):                        0.480\n",
      "Kurtosis:                       2.021   Cond. No.                         3.73\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "results = smf.ols(formula='cost ~ x1 + x2',data=cost_df).fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffca76b8",
   "metadata": {},
   "source": [
    "## 实验设计方差分析\n",
    "### 逻辑\n",
    "1. 对于不同处理分组中的均值进行是否相同的检验\n",
    "\n",
    "### 单因子完全随机化设计\n",
    "1. 一个处理就相当于是一个因子的一个水平\n",
    "2. 该设计中只存在一个因子，并且该因子只有两个水平\n",
    "3. 完全随机化是指将处理随机指派给试验单位"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbb1cb64",
   "metadata": {},
   "source": [
    "$$\n",
    "    \\begin{array}{ll}\n",
    "    H_0 : \\beta_1=\\beta_2=\\cdots=\\beta_{p-1}=0 \\\\\n",
    "    简化模型 ： E(y) = \\beta_0 \\\\\n",
    "    完全模型 ： E(y) = \\beta_0 + \\beta_1 x_1 + \\beta_2 x_2 + \\cdots + \\beta_{p-1} x_{p-1} \\\\\n",
    "    F=\\frac{(SSE_R-SSE_C)/(p-1)}{SSE_C/(n-p)} \\\\\n",
    "    * 其中，p是处理的数量，对应k+1 \n",
    "    \\end{array}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "055c3720",
   "metadata": {},
   "source": [
    "### ANOVA分析\n",
    "又名变异数分析，Analysis of Variance，主要探索连续型因变量与类别型自变量的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "76c1a9b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>148</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>513</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>335</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>643</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>393</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>433</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>216</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>520</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>536</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>353</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>134</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>327</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>723</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>258</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>166</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>380</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>415</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>280</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>594</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>304</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>465</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      y  x1  x2\n",
       "0   148   1   0\n",
       "1   513   0   1\n",
       "2   335   0   0\n",
       "3    76   1   0\n",
       "4   264   0   1\n",
       "5   643   0   0\n",
       "6   393   1   0\n",
       "7   433   0   1\n",
       "8   216   0   0\n",
       "9   520   1   0\n",
       "10   94   0   1\n",
       "11  536   0   0\n",
       "12  236   1   0\n",
       "13  353   0   1\n",
       "14  128   0   0\n",
       "15  134   1   0\n",
       "16  327   0   1\n",
       "17  723   0   0\n",
       "18   55   1   0\n",
       "19  214   0   1\n",
       "20  258   0   0\n",
       "21  166   1   0\n",
       "22  135   0   1\n",
       "23  380   0   0\n",
       "24  415   1   0\n",
       "25  280   0   1\n",
       "26  594   0   0\n",
       "27  153   1   0\n",
       "28  304   0   1\n",
       "29  465   0   0"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.api as sm\n",
    "\n",
    "weariness_x1_ori = [1,0,0]\n",
    "weariness_x2_ori = [0,1,0]\n",
    "weariness_y = np.array([148,513,335,76,264,643,393,433,216,520,94,536,\n",
    "                        236,353,128,134,327,723,55,214,258,166,135,380,\n",
    "                        415,280,594,153,304,465])\n",
    "weariness_x1 = np.array(weariness_x1_ori * 10)\n",
    "weariness_x2 = np.array(weariness_x2_ori * 10)\n",
    "\n",
    "weariness_df = pd.DataFrame({'y': weariness_y,\n",
    "                             'x1': weariness_x1,\n",
    "                             'x2': weariness_x2})\n",
    "weariness_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "fbfa4a18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.222\n",
      "Model:                            OLS   Adj. R-squared:                  0.165\n",
      "Method:                 Least Squares   F-statistic:                     3.862\n",
      "Date:                Thu, 24 Mar 2022   Prob (F-statistic):             0.0335\n",
      "Time:                        19:36:40   Log-Likelihood:                -193.82\n",
      "No. Observations:                  30   AIC:                             393.6\n",
      "Df Residuals:                      27   BIC:                             397.9\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    427.8000     51.588      8.293      0.000     321.951     533.649\n",
      "x1          -198.2000     72.956     -2.717      0.011    -347.893     -48.507\n",
      "x2          -136.1000     72.956     -1.866      0.073    -285.793      13.593\n",
      "==============================================================================\n",
      "Omnibus:                        1.496   Durbin-Watson:                   2.385\n",
      "Prob(Omnibus):                  0.473   Jarque-Bera (JB):                1.128\n",
      "Skew:                           0.228   Prob(JB):                        0.569\n",
      "Kurtosis:                       2.167   Cond. No.                         3.73\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "weariness_res = smf.ols(formula='y ~ x1 + x2',data=weariness_df).fit()\n",
    "print(weariness_res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "c89e75d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               sum_sq    df          F        PR(>F)\n",
      "Intercept  1830128.40   1.0  68.768680  6.688701e-09\n",
      "x1          196416.20   1.0   7.380511  1.136475e-02\n",
      "x2           92616.05   1.0   3.480129  7.301110e-02\n",
      "Residual    718546.10  27.0        NaN           NaN\n"
     ]
    }
   ],
   "source": [
    "table = sm.stats.anova_lm(weariness_res,typ=3)\n",
    "print(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "2dc1b1ba",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.OLS at 0x7f95b3414970>"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weariness_res.model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49cc9d63",
   "metadata": {},
   "source": [
    "### 手动计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "id": "ffb356be",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x1_y</th>\n",
       "      <th>x2_y</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>5</th>\n",
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       "      <th>6</th>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>166</td>\n",
       "      <td>135</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>415</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>153</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x1_y  x2_y\n",
       "0   148   513\n",
       "1    76   264\n",
       "2   393   433\n",
       "3   520    94\n",
       "4   236   353\n",
       "5   134   327\n",
       "6    55   214\n",
       "7   166   135\n",
       "8   415   280\n",
       "9   153   304"
      ]
     },
     "execution_count": 438,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weariness_ss_df = pd.DataFrame({'x1_y': weariness_df.loc[weariness_df.index.values%3==0,['y']]['y'].to_list(),\n",
    "                                'x2_y': weariness_df.loc[weariness_df.index.values%3==1,['y']]['y'].to_list()})\n",
    "weariness_ss_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "id": "ecc7c006",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x1_y</th>\n",
       "      <th>x2_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>229.600000</td>\n",
       "      <td>291.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>158.196221</td>\n",
       "      <td>126.787705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>94.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>137.500000</td>\n",
       "      <td>226.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>159.500000</td>\n",
       "      <td>292.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>353.750000</td>\n",
       "      <td>346.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>520.000000</td>\n",
       "      <td>513.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             x1_y        x2_y\n",
       "count   10.000000   10.000000\n",
       "mean   229.600000  291.700000\n",
       "std    158.196221  126.787705\n",
       "min     55.000000   94.000000\n",
       "25%    137.500000  226.500000\n",
       "50%    159.500000  292.000000\n",
       "75%    353.750000  346.500000\n",
       "max    520.000000  513.000000"
      ]
     },
     "execution_count": 440,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weariness_ss_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "id": "fc525899",
   "metadata": {},
   "outputs": [],
   "source": [
    "def SSE(data):\n",
    "    mean = data.mean()\n",
    "    SSE_m = (data-mean) * (data-mean)\n",
    "    SSE_sum_m = np.sum(SSE_m, axis=0)\n",
    "    SSE = np.sum(SSE_sum_m)\n",
    "    return SSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "id": "6097c75e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "718546.1000000001"
      ]
     },
     "execution_count": 436,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SSE(weariness_ss_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "id": "d13531b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['y', 'x1', 'x2'], dtype='object')"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weariness_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "id": "382ff408",
   "metadata": {},
   "outputs": [],
   "source": [
    "def anova(data,y):\n",
    "    '''\n",
    "    data ~ input matrix\n",
    "    y ~ output (n,) vector\n",
    "    return ~ beta (n,) vector and SSE MSE value\n",
    "    '''\n",
    "    XTX = np.dot(data.T,data)\n",
    "    XY = np.dot(data.T,y)\n",
    "    beta = np.dot(np.linalg.inv(XTX),XY)\n",
    "    YTY = np.dot(y.T,y)\n",
    "    bXY = np.dot(beta.T,XY)\n",
    "    SSE = YTY - bXY\n",
    "    MSE = SSE/len(data.columns)\n",
    "    return [beta, SSE, MSE]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "id": "baf08d34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([229.6, 291.7]), 2548674.5, 1274337.25]"
      ]
     },
     "execution_count": 418,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova(weariness_df[['x1','x2']],weariness_df['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "id": "4c209603",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([335, 643, 216, 536, 128, 723, 258, 380, 594, 465])"
      ]
     },
     "execution_count": 429,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x3 = weariness_df.loc[weariness_df.index.values%3==2,['y']]['y'].to_numpy()\n",
    "x3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "id": "3ae7c945",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['y', 'x1', 'x2'], dtype='object')"
      ]
     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weariness_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "id": "4bb252cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "578064.4999999995 369910.5 208153.99999999953\n",
      "205542.86666666658 718546.1000000001 924088.9666666667\n",
      "#######################\n",
      "3.861726756293018\n",
      "3.861726756293018\n"
     ]
    }
   ],
   "source": [
    "x = weariness_df[['x1','x2']]\n",
    "x_feed = sm.add_constant(x)\n",
    "y = np.dot(weariness_res.params,x_feed.T)\n",
    "SSE_R = np.sum((y-x3.mean())**2)\n",
    "print(SSE_R,SSE(weariness_ss_df),SSE_R - SSE(weariness_ss_df))\n",
    "print(weariness_res.mse_model*2, weariness_res.mse_resid*27, weariness_res.mse_total*29)\n",
    "print('#######################')\n",
    "print(weariness_res.mse_model/weariness_res.mse_resid)\n",
    "print(weariness_res.fvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a976668",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e14512c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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